Artificial intelligence (AI) network enhancements with peer-to-peer links

Abstract

An artificial intelligence (AI) edge user equipment (AEU) may establish one or more peer-to-peer connections (P2P) with the UE via a discovery procedure to form a connected UE; transmitting capability information of each of the one or more connected UEs to a base station (BS) in a first control message; transmitting configuration information to each of the one or more connected UEs via the one or more P2P connections; receiving data from each of the one or more connected UEs via the one or more P2P connections based on the configuration information; and transmitting, to the BS via a third control message, the collected data from one or more of the one or more connected UEs for one or more artificial intelligence (AI) models.

Description

Technical Field

[0001] Embodiments of the present invention relate to wireless communication, including apparatus, systems, and methods for enhancing 6G artificial intelligence (AI) networks using peer-to-peer links, including systems, methods, and mechanisms.

[0002] Description of related technologies The use of wireless communication systems is growing rapidly. In recent years, wireless devices, such as smartphones and tablets, have become increasingly sophisticated. In addition to supporting telephone calls, many mobile devices now offer access to the internet, email, text messaging, and navigation using the Global Positioning System (GPS), and can operate complex applications that utilize these functions. Furthermore, many different wireless communication technologies and standards exist.

[0003] Long Term Evolution (LTE), also known as Evolved Universal Terrestrial Radio Access Network (E-UTRAN), has become the technology of choice for most wireless network operators worldwide, providing mobile broadband data and high-speed internet access to their subscriber base. LTE was first proposed in 2004 as an upgrade to the fourth generation (4G) of the Third Generation Partnership Project (3GPP) and was first standardized in 2008. Since then, with the exponential growth in the use of wireless communication systems, the demand from wireless network operators for higher capacity to support higher density mobile broadband users has also increased. Therefore, research into new radio access technologies began in 2015, and in 2017, the first version of 3GPP's Fifth Generation New Radio (5G NR) was standardized. The fifth-generation mobile network, or fifth-generation wireless system, is called 3GPP NR (or 5G-NR or NR-5G for 5G New Radio, or simply NR). NR provides higher capacity for higher density mobile broadband users while supporting device-to-device, ultra-reliable, and massive machine-type communications, as well as lower latency and lower battery consumption than the LTE standard.

[0004] Compared to LTE, 5G-NR offers higher capacity for higher density mobile broadband users, while also supporting ultra-reliable and massive machine-type communication between devices, as well as lower latency and / or lower battery consumption. Furthermore, NR allows for more flexible UE scheduling compared to current LTE. Therefore, ongoing development of 5G-NR is underway to leverage the potentially higher throughput at higher frequencies.

[0005] One aspect of wireless communication systems (e.g., systems used for NR cellular wireless communication) is the transmission and measurement of signals, which can be used to train and enhance the wireless communication system using artificial intelligence. In 5G-NR, artificial intelligence (AI) models can be used to predict patterns and reduce the overhead of transmitting and measuring signals. However, for wireless devices not connected to a cellular network, the ability to gain enhancements through the use of AI models is limited. Summary of the Invention

[0006] The implementation plan relates to wireless communication, including devices, systems, and methods for providing artificial intelligence (AI) network enhancements using peer-to-peer connections.

[0007] In some implementations, an Artificial Intelligence (AI) Edge User Equipment (AEU) may establish one or more peering connections with one or more UEs via a discovery process to form connected UEs; send capability information of each of the one or more connected UEs to a Base Station (BS) in a first control message, wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU; receive configuration information from the BS in a second control message for reporting data from the one or more connected UEs; send the configuration information to each of the one or more connected UEs via the one or more peering connections; receive data from each of the one or more connected UEs via the one or more peering connections based on the configuration information; and send the collected data from each of the one or more connected UEs to the BS via a third control message for use in one or more Artificial Intelligence (AI) models.

[0008] Further embodiments of an apparatus relating to an AI edge user equipment (AEU) configured to enhance the performance of an artificial intelligence (AI) network are disclosed. The apparatus includes one or more processors coupled to memory. The one or more processors are configured to establish one or more peer connections with one or more user equipments (UEs) via a discovery process to form connected UEs. The AEU may send capability information of each connected UE to a base station (BS) in a first control message, wherein the capability information includes one or more of the following: a UE identifier (ID), an indication of whether each UE is within or outside the coverage of the BS, a first confirmation of willingness to share data with the AEU, or a second confirmation of willingness to cooperate with the AEU. The AEU may receive configuration information from the BS in a second control message for reporting data from the UEs of these connections. The AEU may send this configuration information to each connected UE via these peer connections. The AEU may receive data from each connected UE via these peer connections based on this configuration. Finally, the AEU may send the data collected from each connected UE to the BS in a third control message for use in one or more AI models.

[0009] In this discovery process, the AEU receives request messages from the UE requesting AI / ML offloading capabilities, decodes these capabilities, measures the received signal strength, determines whether the received signal strength exceeds a threshold, and selects a UE to establish a peering connection based on the threshold. These peering connections include various radio access technologies, such as direct wireless local area network (WLAN) connections. Using this peering connection, the first control message indicates the UEs connected and their connection types. The second control message contains a transparent container with configuration and an AI model. These configurations can be received via signaling. An AI model for local training / inference can also be received. The third message contains a transparent container with the data, training results, inference results, aggregated results, and the final aggregated inference result.

[0010] The techniques described herein can be implemented in and / or used with a variety of different types of devices, including but not limited to base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, vehicles, and various other computing devices.

[0011] The present invention is intended to provide a brief overview of some of the subjects described in this document. Therefore, it should be understood that the above features are merely illustrative and should not be construed as narrowing the scope or substance of the subjects described herein in any way. Other features, aspects, and advantages of the subjects described herein will become apparent from the following detailed description, drawings, and claims. Attached Figure Description

[0012] A better understanding of the subject matter can be obtained by considering the following detailed description of various embodiments in conjunction with the accompanying drawings, in which: Figure 1A Example wireless communication systems according to some implementation schemes are illustrated.

[0013] Figure 1B Examples of base stations and access points communicating with user equipment (UE) devices according to some implementation schemes are illustrated.

[0014] Figure 2 Example block diagrams of base stations according to some implementation schemes are shown.

[0015] Figure 3 Example block diagrams of servers according to some implementation schemes are shown.

[0016] Figure 4 Example block diagrams of a UE according to some implementation schemes are shown.

[0017] Figure 5 Example block diagrams of cellular communication circuits according to some implementation schemes are shown.

[0018] Figure 6A Examples of 5G network architectures according to some implementation schemes are illustrated, which combine both 3GPP (e.g., cellular) and non-3GPP (e.g., non-cellular) access to 5GCN.

[0019] Figure 6B Examples of 5G network architectures according to some implementation schemes are illustrated, which combine dual 3GPP access to 5GCN (e.g., LTE and 5G NR) and non-3GPP access.

[0020] Figure 7 Examples of baseband processor architectures for UEs according to some implementation schemes are illustrated.

[0021] Figure 8 Examples of devices according to some implementation schemes are shown.

[0022] Figure 9 Example baseband circuits according to some implementation schemes are illustrated.

[0023] Figure 10 Examples of architectures for AI-enabled systems, including O-RAN, are illustrated according to some implementation schemes.

[0024] Figures 11A to 11C An example of an AI / ML edge user apparatus (AEU) deployed to enhance AI / ML performance using sidelink connectivity is illustrated.

[0025] Figure 12 Examples of signaling for implementing AI / ML network enhancements utilizing sidelinks are illustrated according to some implementation schemes.

[0026] Figure 13 Example timing diagram signaling between user equipment (UE), AI / ML edge user equipment (AEU), and base station (BS) according to some implementation schemes is illustrated.

[0027] Figure 14 Examples of system architectures supporting AI / ML network enhancements using sidelinks are illustrated according to some implementation schemes.

[0028] Figure 15 Examples of AI / ML edge user equipment (AEU) selection processes that support AI / ML network enhancements using sidelinks are illustrated according to some implementation schemes.

[0029] Figure 16 Examples of discovery messages for multi-radio, multi-vendor AI / ML perception discovery are illustrated according to some implementation schemes.

[0030] Figure 17 The AEU selection process is implemented according to some implementation schemes to support multi-radio, multi-vendor AI / ML perception discovery.

[0031] Figure 18 According to some implementation schemes, the AEU selection process is performed from the perspective of AEU to support multi-radio, multi-vendor AI / ML perception discovery.

[0032] Figure 19 Examples of methods for using sidelinks to provide enhanced AI / ML performance with AI / ML edge user equipment (AEU) according to some implementation schemes are illustrated.

[0033] Although the features described herein may be subject to various modifications and alternatives, specific embodiments thereof are shown by way of example in the accompanying drawings and described in detail herein. However, it should be understood that the drawings and their detailed description are not intended to limit one to the specific forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the substance and scope of the subject matter as defined by the appended claims. Detailed Implementation

[0034] The following is a glossary of terms used in this disclosure: Memory media—any of various types of nontransitory memory devices or storage devices. The term "memory media" is intended to include mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory; magnetic media, such as hard disk drives or optical storage devices; registers or other similar types of memory elements, etc. Memory media may also include other types of nontransitory memory or combinations thereof. Furthermore, memory media may reside in a first computer system executing a program, or may reside in a different second computer system connected to the first computer system via a network such as the Internet. In the latter example, the second computer system may provide program instructions to the first computer for execution. The term "memory media" may include two or more memory media residing in different locations in different computer systems connected via, for example, a network. Memory media may store program instructions (e.g., embodied in a computer program) that can be executed by one or more processors.

[0035] Carrier media—memory media as described above, and physical transmission media such as buses, networks, and / or other physical transmission media that transmit signals such as electrical signals, electromagnetic signals, or digital signals.

[0036] Programmable hardware elements—including a variety of hardware devices comprising multiple programmable functional blocks connected via programmable interconnects. Examples include FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), FPOAs (Field-Programmable Object Arrays), and CPLDs (Complex PLDs). Programmable functional blocks can range from fine-grained (combinational logic or lookup tables) to coarse-grained (arithmetic logic units or processor cores). Programmable hardware elements may also be referred to as “configurable logic units.”

[0037] Computer system (or computer) — any of the various types of computing or processing systems, including personal computer systems (PCs), mainframe computer systems, workstations, network appliances, internet-connected appliances, personal digital assistants (PDAs), television systems, grid computing systems, or other devices or combinations thereof. In general, the term "computer system" can be broadly defined to encompass any device (or combination of devices) having at least one processor that executes instructions from a memory medium.

[0038] User equipment (UE) (or “UE device”) — any of various types of computer system devices that are mobile or portable and perform wireless communication. Examples of UE devices include mobile phones or smartphones (e.g., iPhone). ™Based on Android ™ Telephones), portable gaming devices (e.g., Nintendo DS) ™ PlayStation Portable ™ Gameboy Advance ™ iPhone ™ ), laptops, wearable devices (e.g., smartwatches, smart glasses), PDAs, portable internet devices, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones), UAV controllers (UACs), etc. Generally speaking, the term "UE" or "UE device" can be broadly defined to encompass any electronic device, computing device, and / or telecommunications device (or combination of devices) that is easily transportable by the user and capable of wireless communication.

[0039] Base station—The term “base station” has the full range of its common meaning and includes at least a wireless communication station that is installed in a fixed location and is used for communication as part of a wireless telephone system or radio system.

[0040] A processing element (or processor) refers to a variety of elements or combinations of elements capable of performing functions in a device such as user equipment or cellular network equipment. A processing element may include, for example: a processor and associated memory, portions or circuitry of individual processor cores, an entire processor core, a processor array, circuitry such as an ASIC (Application-Specific Integrated Circuit), programmable hardware elements such as a Field-Programmable Gate Array (FPGA), and any combination thereof. As used herein, the term "one or more processors" may refer to the baseband processing circuitry 804 for a baseband processor (e.g., 804A-804D) or an application processor (e.g., 204).

[0041] A channel is a medium used to transmit information from a transmitter to a receiver. It should be noted that because the characteristics of the term "channel" can vary depending on the wireless protocol, the term "channel" as used herein can be considered to be used in a standard manner consistent with the type of device to which the term is referenced. In some standards, channel width can be variable (e.g., depending on device capabilities, band conditions, etc.). For example, LTE can support scalable channel bandwidths from 1.4 MHz to 20 MHz. In contrast, WLAN channels can be 22 MHz wide, while Bluetooth channels can be 1 MHz wide. Other protocols and standards may include different definitions of channels. Furthermore, some standards may define and use multiple types of channels, for example, different channels for uplink or downlink and / or different channels for different purposes such as data, control information, etc.

[0042] Frequency band—The term “frequency band” has the full range of its general meaning and includes at least a segment of spectrum (e.g., radio frequency spectrum) in which a channel is used or reserved for the same purpose.

[0043] Wi-Fi—The term “Wi-Fi” (or WiFi) has the full range of its usual meaning and includes at least wireless communication networks or RATs, which are provided by and through wireless LAN (WLAN) access points to provide connectivity to the Internet. Most modern Wi-Fi networks (or WLAN networks) are based on the IEEE 802.11 standard and are marketed under the name “Wi-Fi.” Wi-Fi (WLAN) networks are different from cellular networks.

[0044] 3GPP access refers to access technologies (e.g., radio access technologies) specified by the Third Generation Partnership Project (3GPP) standards. These access technologies include, but are not limited to, GSM / GPRS, LTE, LTE-A, and / or 5G NR, 6G, and above. Generally speaking, 3GPP access refers to various types of cellular access technologies.

[0045] Non-3GPP access refers to any access technology (e.g., radio access technologies) not specified by 3GPP standards. These accesses include, but are not limited to, WiMAX, CDMA2000, Wi-Fi, WLAN, and / or fixed networks. Non-3GPP access can be categorized into two types: "trusted" and "untrusted." Trusted non-3GPP access can interact directly with the Evolved Packet Core (EPC) and / or 5G Core (5GC), while untrusted non-3GPP access interoperates with the EPC / 5GC via network entities such as Evolved Packet Data Gateways and / or 5G NR Gateways. Generally speaking, non-3GPP access refers to various types of non-cellular access technologies.

[0046] Automatic—means that an action or operation is performed by a computer system (e.g., software executed by the computer system) or device (e.g., circuits, programmable hardware elements, ASICs, etc.) without requiring direct specification or execution of the action or operation by user input. Therefore, the term "automatically" is the opposite of an operation performed or specified manually by a user, where the user provides input to directly perform the operation. An automatic process may be initiated by user-provided input, but the subsequent actions performed "automatically" are not specified by the user; that is, they are not performed "manually," where the user specifies each action to be performed. For example, a user filling out a form by selecting each field and providing input specifying information (e.g., by typing information, selecting a checkbox, radio selection, etc.) is considered manually filling out the form, even if the computer system can update the form in response to the user's actions. The form can be automatically filled out by a computer system, where the computer system (e.g., software executed on the computer system) analyzes the fields of the form and fills out the form without any user input specifying answers for the fields. As indicated above, the user may invoke the automatic filling of the form but does not participate in the actual filling of the form (e.g., the user does not manually specify answers for the fields, but they are completed automatically). This manual provides various examples of operations that can be performed automatically in response to actions taken by the user.

[0047] Approximately—means a value close to the correct or precise value. For example, approximately could mean a value within 1% to 10% of the precise (or expected) value. However, it should be noted that the actual threshold (or tolerance) can be application-dependent. For example, in some implementations, “approximately” may mean within 0.1% of some specified or expected value, while in various other implementations, the threshold may be, for example, 2%, 3%, 5%, etc., depending on the expectations or uses of a particular application.

[0048] Concurrency refers to the parallel execution or implementation of tasks, processes, or programs in a manner that at least partially overlaps. For example, concurrency can be achieved using “strong” or strict parallelism, where tasks are executed in parallel (at least partially) on corresponding computing elements; or using “weak parallelism,” where tasks are executed in an interleaved manner (e.g., by time multiplexing of execution threads).

[0049] Encoding—refers to the baseband circuitry (e.g., 804) used to encode data. As described herein, the data can also be modulated and prepared for output from the baseband circuitry for transmission.

[0050] Decoding—refers to the baseband circuitry (e.g., 804) used to decode data. As described in this article, after data is received, it can also be demodulated and prepared for decoding.

[0051] Various components can be described as being "configured" to perform one or more tasks. In this context, "configured" is a broad expression generally meaning "having a structure" that performs one or more tasks during operation. Therefore, a component can be configured to perform a task even when it is not currently performing one (e.g., a set of electrical conductors can be configured to electrically connect one module to another, even when the two modules are not connected). In some contexts, "configured" can be a broad expression generally meaning "having a circuit" that performs one or more tasks during operation. Therefore, a component can be configured to perform a task even when it is not currently powered on. Generally, the circuit forming the structure corresponding to "configured" can include hardware circuitry.

[0052] For ease of description, various components may be described as performing one or more tasks. Such descriptions should be interpreted as including the phrase "configured to". Statements describing a component as configured to perform one or more tasks are explicitly intended not to invoke the interpretation of 35 USC § 112(f) for that component.

[0053] Figure 1A and Figure 1B Communication system Figure 1A A simplified exemplary wireless communication system according to some implementation schemes is shown. It should be noted that... Figure 1A The system described herein is merely one example of a possible system, and the features of this disclosure can be implemented in any of a variety of systems as needed.

[0054] As shown in the figure, the example wireless communication system includes a base station 102A, which communicates with one or more user equipments 106A, 106B to 106N via a transmission medium. Each user equipment may be referred to herein as a "user equipment" (UE). Therefore, user equipment 106 is referred to as a UE or UE device.

[0055] Base station (BS) 102A may be a transceiver base station (BTS) or a cell site (“cellular base station”), and may include hardware that enables wireless communication with UE 106A to UE 106N.

[0056] The communication area (or coverage area) of a base station may be referred to as a "cell". Base station 102A and UE 106 can be configured to communicate via a transmission medium using any of various Radio Access Technologies (RATs), also known as wireless communication technologies or telecommunications standards, such as GSM, UMTS (associated with air interfaces such as WCDMA or TD-SCDMA), LTE, LTE-Advanced (LTE-A), 5G New Radio (5G NR), HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD), etc. Note that if base station 102A is implemented in the context of LTE (E-UTRAN), its alternative location may be referred to as an "eNodeB" or "eNB". Note that if base station 102A is implemented in the context of 5G NR, its alternative location may be referred to as a "gNodeB" or "gNB". Note that if base station 102A-102N is implemented in the context of 6G, it may be simply referred to as a base station or BS.

[0057] As shown in the figure, base station 102A can also be configured to communicate with network 100 (e.g., in various possibilities, the core network of a cellular service provider, telecommunications networks such as the Public Switched Telephone Network (PSTN), and / or the Internet). Therefore, base station 102A facilitates communication between user equipments and / or between user equipments and network 100. Specifically, cellular base station 102A can provide UE 106 with various telecommunications capabilities, such as voice, SMS, and / or data services.

[0058] Base station 102A and other similar base stations (such as base stations 102B, ..., 102N) operating according to the same or different cellular communication standards can therefore be provided as a network of cells that can provide continuous or nearly continuous overlapping services to UEs 106A-N and similar devices over a geographical area via one or more cellular communication standards. A managed network (or cell) may include base stations (or evolved nodeBs (eNBs) or next-generation nodeBs (gNBs)) that signal to multiple user equipment (UEs) (or user nodes or terminals) and are operatively connected to a core network (CN) that may be configured to provide non-radio tasks such as management and is typically connected to a larger network such as the Internet.

[0059] Therefore, although base station 102A can act as such Figure 1AThe diagram shows the "serving cell" of UEs 106A-106N, but each UE 106 may also be able to receive signals (and possibly within its communication range) from one or more other cells (which may be provided by base stations 102B-102N and / or any other base stations), which may be referred to as "neighboring cells". Such cells may also facilitate communication between user equipments and / or between user equipments and network 100. These cells may include "macro" cells, "micro" cells, "pecimen" cells, and / or any other cells of various other granularities providing a service area size. For example, Figure 1A The illustrated base stations 102A-102B may be macro cells, while base station 102N may be a micro cell. Other configurations are also possible.

[0060] In some implementations, base station 102A may be a next-generation base station, such as a 5G New Radio (5G NR) base station or “gNB”, or a base station configured to communicate in a sixth-generation (6G) radio network. In some implementations, BS102A may connect to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network. Furthermore, the BS cell may include one or more transition and receive points (TRPs). Additionally, a UE capable of operating according to 5G NR may connect to one or more TRPs within one or more BS 102A.

[0061] It should be noted that UE 106 may be able to communicate using multiple wireless communication standards. For example, UE 106 may be configured to communicate using wireless networking (e.g., Wi-Fi) and / or peer-to-peer wireless communication protocols (e.g., Bluetooth, Wi-Fi peer-to-peer, etc.) other than at least one cellular communication protocol (e.g., GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces), LTE, LTE-A, 5G NR, HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD, etc.)). If desired, UE 106 may also be configured, or alternatively, to communicate using one or more Global Navigation Satellite Systems (GNSS, such as GPS or GLONASS), one or more mobile television broadcasting standards (e.g., ATSC-M / H or DVB-H) and / or any other wireless communication protocol. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible.

[0062] Figure 1BUser equipment 106 (e.g., one of devices 106A to 106N) communicating with base station 102 and access point 112 according to some embodiments is illustrated. UE 106 can be a device with cellular and non-cellular communication capabilities (e.g., Bluetooth, Wi-Fi, etc.), such as a mobile phone, handheld device, computer or tablet, or virtually any type of wireless device.

[0063] UE 106 may include a processor configured to execute program instructions stored in memory. UE 106 may execute any method implementation of the method embodiments described herein by executing such stored instructions. Alternatively or additionally, UE 106 may include programmable hardware elements, such as a field-programmable gate array (FPGA) configured to execute any method implementation of the method embodiments described herein or any portion thereof.

[0064] UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies. In some embodiments, UE 106 may be configured to communicate using, for example, CDMA2000 (1xRTT / 1xEV-DO / HRPD / eHRPD), LTE / Advanced LTE, or 5G NR and / or GSM, LTE, Advanced LTE, or 5G NR using a single shared radio component. The shared radio component may be coupled to a single antenna or to multiple antennas (e.g., for MIMO) for performing wireless communication. Generally, the radio component may include any combination of baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc.) or digital processing circuitry (e.g., for digital modulation and other digital processing). Similarly, the radio component may use the aforementioned hardware to implement one or more receive chains and transmit chains. For example, UE 106 may share one or more portions of the receive chain and / or transmit chain among multiple wireless communication technologies (such as those discussed above).

[0065] In some implementations, UE 106 may include independent transmit and / or receive chains (e.g., including independent antennas and other radio components) for each wireless communication protocol configured to communicate therewith. As another possibility, UE 106 may include one or more radio components shared among multiple wireless communication protocols, as well as one or more radio components uniquely used by a single wireless communication protocol. For example, UE 106 may include shared radio components for communication using either LTE (E-UTRAN) or 5G NR (or LTE or 1xRTT, or LTE or GSM), and independent radio components for communication using each of Wi-Fi and Bluetooth. Other configurations are also possible.

[0066] Figure 2 Block diagram of a base station Figure 2 Example block diagrams of base station 102 according to some implementation schemes are shown. It should be noted that... Figure 3 The base station shown is merely one example of a possible base station. As illustrated, base station 102 may include processor 204, which executes program instructions for base station 102. Processor 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from processor 204 and translate those addresses into locations in memory (e.g., memory 260 and read-only memory (ROM) 250) or into other circuitry or devices.

[0067] Base station 102 may include at least one network port 270. Network port 270 may be configured to couple to a telephone network and provide access to multiple devices, such as UE device 106, as described above in Figure 1 and... Figure 2 Access to the telephone network described in the text.

[0068] Network port 270 (or an additional network port) may also be configured, or alternatively configured, to couple to a cellular network, such as the core network of a cellular service provider. The core network may provide mobility-related services and / or other services to multiple devices, such as UE device 106. In some cases, network port 270 may be coupled to a telephone network via the core network, and / or the core network may provide a telephone network (e.g., between other UE devices served by a cellular service provider).

[0069] In some implementations, base station 102 may be a next-generation base station, such as a 5G New Radio (5G NR) base station, or "gNB". In such implementations, base station 102 may be connected to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network. Furthermore, base station 102 may be considered a 5G NR cell and may include one or more transition and receive points (TRPs). Additionally, a UE capable of operating according to 5G NR may connect to one or more TRPs within one or more BSs.

[0070] Base station 102 may include at least one antenna 234, and possibly multiple antennas. At least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UE device 106 via radio component 230. Antenna 234 communicates with radio component 230 via communication link 232. Communication link 232 may be a receive link, a transmit link, or both. Radio component 230 may be configured to communicate via various wireless communication standards, including but not limited to 5G NR, LTE, LTE-A, GSM, UMTS, CDMA2000, Wi-Fi, etc.

[0071] Base station 102 can be configured to perform wireless communication using multiple wireless communication standards. In some cases, base station 102 may include multiple radio components that enable base station 102 to communicate according to multiple wireless communication technologies. For example, as one possibility, base station 102 may include an LTE radio component for performing communication according to LTE and a 5G NR radio component for performing communication according to 5G NR. In this case, base station 102 may be able to operate as both an LTE base station and a 5G NR base station. As another possibility, base station 102 may include a multimode radio component capable of performing communication according to any of multiple wireless communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc.).

[0072] Base station 102N can communicate with UE 106N via radio links, which can be implemented as any suitable type of radio link. Radio links may include downlinks transmitting data and control information from base station 102N to UE 106N, uplinks transmitting other data and control information from UE 106N to BS 102N, or both. Radio links may include one or more radio links or bearers implemented using any suitable communication protocol or standard, or a combination of communication protocols or standards (such as 3GPP LTE, 5G NR, 6G, etc.). Multiple radio links may be aggregated in carrier aggregation to provide higher data rates for UE 106N. Multiple radio links from multiple BS 102Ns may be configured for Coordinated Multipoint (CoMP) communication with UE 106N. Additionally, multiple radio links may be configured for single radio access technology (RAT) dual connectivity (single RAT-DC) or multiple RAT dual connectivity (MR-DC).

[0073] BS 102N can be collectively referred to as Radio Access Network 100 (RAN, Evolved Universal Terrestrial Radio Access Network, E-UTRAN, 5G NR RAN or NR RAN, 6G RAN). BS 102N within RAN 100 can connect to a core network, such as a fifth-generation core (5GC) or a 6G core network (e.g., Figure 10 (1004). Base station 102N can connect to core network 1004 via NG2 interface (or a similar 6G interface) for control plane signaling and via NG3 interface (or a similar 6G interface) for user plane data communication. In addition to connecting to the core network, base station 102N can also communicate with each other via Xn Application Protocol (XnAP) to exchange user plane and control plane data. UE 106N can also connect to public networks, such as Internet 600 (Figure 6), via the core network.

[0074] As further described herein, BS 102 may include hardware and software components for implementing or supporting specific implementations of the features described herein. The processor 204 of base station 102 may be configured, for example, to implement or support some or all of the methods described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, processor 204 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array), or as an ASIC (Application-Specific Integrated Circuit), or a combination thereof. Alternatively (or further), in conjunction with one or more of other components 230, 232, 234, 240, 250, 260, 270, the processor 204 of BS 102 may be configured to implement or support some or all of the features described herein.

[0075] Furthermore, as described herein, processor 204 may comprise one or more processing elements. In other words, one or more processing elements may be included in processor 204. Therefore, processor 204 may include one or more integrated circuits (ICs) configured to perform the functions of processor 204. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 204.

[0076] Furthermore, as described herein, radio component 230 may comprise one or more processing elements. In other words, radio component 230 may include one or more processing elements. Therefore, radio component 230 may include one or more integrated circuits (ICs) configured to perform the functions of radio component 230. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of radio component 230.

[0077] Figure 3 Server block diagram Figure 3 Example block diagrams of server 104 according to some implementation schemes are shown. Note that... Figure 3 The server shown is merely one example of a possible server. As illustrated, server 104 may include processor 344 capable of executing program instructions for server 104. Processor 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from processor 344 and translate those addresses into locations in memory (e.g., memory 364 and read-only memory (ROM) 354) or into other circuitry or devices.

[0078] Server 104 can be configured to provide access to network functions to multiple devices, such as base station 102 and UE device 106, for example, as further described herein.

[0079] In some implementations, server 104 may be part of a radio access network, such as 4G EUTRAN, 5G New Radio (5G NR) radio access network, or 6G RAN. In some implementations, server 104 may be connected to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network.

[0080] As further described herein, server 104 may include hardware and software components for implementing or supporting the implementation of the features described herein. Processor 344 of server 104 may be configured, for example, to implement or support some or all of the methods described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium). Alternatively, processor 344 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array), or as an ASIC (Application-Specific Integrated Circuit), or a combination thereof. Alternatively (or in addition), in combination with one or more of other components 354, 364, and / or 374, processor 344 of server 104 may be configured to implement or support some or all of the features described herein.

[0081] Furthermore, as described herein, processor 344 may comprise one or more processing elements. In other words, one or more processing elements may be included in processor 344. Therefore, processor 344 may include one or more integrated circuits (ICs) configured to perform the functions of processor 344. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 344.

[0082] Figure 4 : UE block diagram Figure 4 A simplified block diagram of a communication device 106 according to some implementation schemes is shown. Note that... Figure 4 The block diagram of the communication device is merely one example of possible communication devices. According to implementations, communication device 106 may be a user equipment (UE) device, mobile device or mobile station, wireless device or wireless station, desktop computer or computing device, mobile computing device (e.g., laptop computer, notebook computer, or portable computing device), tablet computer, unmanned aerial vehicle (UAV), UAV controller (UAC), and / or a combination of devices, and other devices. As shown, communication device 106 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system-on-a-chip (SOC), which may include portions for various purposes. Alternatively, the set of components 400 may be implemented as individual components or groups of components for various purposes. The set of components 400 may be (e.g., communicatively; directly or indirectly) coupled to various other circuitry of communication device 106.

[0083] For example, communication device 106 may include various types of memory (e.g., including NAND flash memory 410), input / output interfaces such as connector I / F 420 (e.g., for connection to a computer system; docking station; charging station; input devices such as microphone, camera, keyboard; output devices such as speaker; etc.), a display 460 that may be integrated with or external to communication device 106, and cellular communication circuitry 430 such as for 6G, 5G NR, LTE, GSM, etc., and short- to medium-range wireless communication circuitry 429 (e.g., Bluetooth). ™ (and WLAN circuitry). In some embodiments, communication device 106 may include wired communication circuitry (not shown), such as a network interface card for Ethernet, for example. Communication device 106 may be configured for direct wireless networking between UEs, such as Wi-Fi Direct.

[0084] Cellular communication circuitry 430 may be coupled (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as shown. Short-to-medium-range wireless communication circuitry 429 may also be coupled (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 as shown. Alternatively, short-to-medium-range wireless communication circuitry 429 may be coupled (e.g., communicatively; directly or indirectly) to antennas 437 and 438 in addition to (e.g., communicatively; directly or indirectly) coupling to antennas 435 and 436, or as an alternative to coupling to these antennas. Short-to-medium-range wireless communication circuitry 429 and / or cellular communication circuitry 430 may include multiple receive chains and / or multiple transmit chains for receiving and / or transmitting multiple spatial streams, such as in a multiple-input multiple-output (MIMO) configuration.

[0085] In some embodiments, as further described below, the cellular communication circuit 430 may include dedicated receive chains for multiple RATs (including and / or (e.g., communicatively; directly or indirectly) coupled to dedicated processors and / or radio components) (e.g., a first receive chain for LTE and a second receive chain for 5G NR or 6G). Furthermore, in some embodiments, the cellular communication circuit 430 may include a single transmit chain that can be switched between radio components dedicated to a particular RAT. For example, a first radio component may be dedicated to a first RAT (e.g., LTE) and can communicate with a dedicated receive chain and a transmit chain shared with an additional radio component, such as a second radio component that may be dedicated to a second RAT (e.g., 5G NR or 6G) and can communicate with both the dedicated receive chain and the shared transmit chain.

[0086] The communication device 106 may also include one or more user interface elements and / or be configured for use with one or more user interface elements. The user interface elements may include any of a variety of elements, such as a display 460 (which may be a touch screen display), a keyboard (which may be a separate keyboard or may be implemented as part of the touch screen display), a mouse, a microphone and / or a speaker, one or more cameras, one or more buttons, and / or any of a variety of other elements capable of providing information to the user and / or receiving or interpreting user input.

[0087] The communication device 106 may also include one or more smart cards 445 containing SIM (Subscriber Identity Module) functionality, such as one or more UICC (Universal Integrated Circuit Card) cards 445. It should be noted that the term "SIM" or "SIM entity" is intended to include any of various types of SIM implementations or SIM functions, such as one or more UICC cards 445, one or more eUICCs, one or more eSIMs, removable or embedded, etc. In some embodiments, the UE 106 may include at least two SIMs. Each SIM may execute one or more SIM applications and / or otherwise implement SIM functionality. Thus, each SIM may be a single smart card that can be embedded, for example, soldered to a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card. Therefore, a SIM may be one or more removable smart cards (such as UICC cards, sometimes referred to as "SIM cards"), and / or SIM 410 may be one or more embedded cards (such as embedded UICCs (eUICCs), sometimes referred to as "eSIMs" or "eSIM cards"). In some implementations (such as when the SIM includes an eUICC), one or more SIMs within the SIM can implement embedded SIM (eSIM) functionality; in such implementations, a single SIM within the SIM can execute multiple SIM applications. Each SIM may include components such as a processor and / or memory; instructions for performing SIM / eSIM functionality may be stored in memory and executed by the processor. In some implementations, UE 106 may include, as needed, a combination of removable smart cards and fixed / non-removable smart cards (such as one or more eUICC cards implementing eSIM functionality). For example, UE 106 may include two embedded SIMs, two removable SIMs, or a combination of one embedded SIM and one removable SIM. Various other SIM configurations are also envisioned.

[0088] As described above, in some implementations, UE 106 may include two or more SIMs. Including two or more SIMs in UE 106 allows UE 106 to support two different phone numbers and allows UE 106 to communicate on two or more corresponding networks. For example, the first SIM may support a first RAT such as LTE, and the second SIM 106 may support a second RAT such as 5G NR or 6G. Other specific implementations and RATs are also possible. In some implementations, when UE 106 includes two SIMs, UE 106 may support Dual SIM Dual Standby (DSDA) functionality. DSDA functionality allows UE 106 to connect to two networks simultaneously (and use two different RATs), or allows two connections supported by two different SIMs using the same or different RATs to be maintained simultaneously on the same or different networks. DSDA functionality also allows UE 106 to receive voice calls or data traffic simultaneously on either phone number. In some implementations, voice calls may be packet-switched communications. In other words, voice calls can be received using LTE-based Voice (VoLTE) technology and / or NR-based Voice (VoNR) technology. In some implementations, UE 106 may support Dual SIM Dual Standby (DSDS) functionality. DSDS functionality allows either of the two SIMs in UE 106 to remain in standby while awaiting a voice call and / or data connection. In DSDS, when a call / data connection is established on one SIM, the other SIM is no longer active. In some implementations, DSDx functionality (DSDA or DSDS functionality) can be implemented using a single SIM (e.g., eUICC) that performs multiple SIM applications for different carriers and / or RATs.

[0089] As shown in the figure, the SOC 400 may include a processor 402 and display circuitry 404. The processor executes program instructions of the communication device 106, and the display circuitry performs graphics processing and provides display signals to the display 460. The processor 402 may also be coupled to a memory management unit (MMU) 440, which may be configured to receive addresses from the processor 402 and translate those addresses into locations in memory (e.g., memory 406, read-only memory (ROM) 450, NAND flash memory 410); and / or coupled to other circuitry or devices, such as display circuitry 404, short-to-mid-range wireless communication circuitry 429, cellular communication circuitry 430, connector I / F 420, and / or display 460. The MMU 440 may be configured to perform memory protection and page table translation or setup. In some embodiments, the MMU 440 may be included as part of the processor 402.

[0090] As noted above, communication device 106 can be configured to communicate using wireless and / or wired communication circuits. Communication device 106 can be configured to perform methods for AI-based CSI feedback with CSI prediction, including systems, methods, and mechanisms for instructing predicted CSI reports, network configuration for CSI feedback, UE PMI report formats, and AI model lifecycle management for UEs, such as in 5G NR systems, 6G systems, and above, as further described herein.

[0091] As described herein, communication device 106 may include hardware and software components for implementing the features described above to communicate a scheduling profile for power saving to the network. For example, by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium), processor 402 of communication device 106 may be configured to implement some or all of the features described herein. Alternatively (or further), processor 402 may be configured as a programmable hardware element (such as a FPGA (Field-Programmable Gate Array)) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or further), in conjunction with one or more of other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460, processor 402 of communication device 106 may be configured to implement some or all of the features described herein.

[0092] Furthermore, as described herein, processor 402 may include one or more processing elements. Therefore, processor 402 may include one or more integrated circuits (ICs) configured to perform the functions of processor 402. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 402.

[0093] Furthermore, as described herein, the cellular communication circuit 430 and the short-to-mid-range wireless communication circuit 429 may each include one or more processing elements. In other words, one or more processing elements may be included in the cellular communication circuit 430, and similarly, one or more processing elements may be included in the short-to-mid-range wireless communication circuit 429. Therefore, the cellular communication circuit 430 may include one or more integrated circuits (ICs) configured to perform the functions of the cellular communication circuit 430. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of the cellular communication circuit 430. Similarly, the short-to-mid-range wireless communication circuit 429 may include one or more ICs configured to perform the functions of the short-to-mid-range wireless communication circuit 429. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of the short-to-mid-range wireless communication circuit 429.

[0094] Figure 5 Block diagram of cellular communication circuit Figure 5 Simplified block diagrams of cellular communication circuits according to some implementation schemes are shown. Note that... Figure 5 The block diagram of the cellular communication circuit is merely one example of a possible cellular communication circuit. According to the implementation, the cellular communication circuit 530 (which may be the cellular communication circuit 430) may be included in a communication device such as the communication device 106 described above. As noted above, among other devices, the communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop computer, notebook computer, or portable computing device), a tablet computer, and / or a combination of these devices.

[0095] The cellular communication circuit 530 may (e.g., communicatively; directly or indirectly) be coupled to one or more antennas, such as ( Figure 4 Antennas 435a-435b and 436 are shown in the diagram. In some embodiments, cellular communication circuitry 530 may include dedicated receive chains for various RATs (including and / or coupled to (e.g., communicative ground; directly or indirectly) coupled to dedicated processors and / or radio components) (e.g., a first receive chain for LTE and a second receive chain for 5G NR or 6G). For example, as... Figure 5 As shown, the cellular communication circuit 530 may include a modem 510 and a modem 520. The modem 510 may be configured for communication according to a first RAT (e.g., such as LTE or LTE-A), and the modem 520 may be configured for communication according to a second RAT (e.g., such as 5G NR or 6G).

[0096] As shown, modem 510 may include one or more processors 512 and memory 516 communicating with processors 512. Modem 510 may communicate with radio frequency (RF) front end 530. RF front end 530 may include circuitry for transmitting and receiving radio signals. For example, RF front end 530 may include receiver circuitry (RX) 532 and transmitter circuitry (TX) 534. In some embodiments, receiver circuitry 532 may communicate with downlink (DL) front end 550, which may include circuitry for receiving radio signals via antenna 335a.

[0097] Similarly, modem 520 may include one or more processors 522 and memory 526 communicating with processor 522. Modem 520 may communicate with RF front end 540. RF front end 540 may include circuitry for transmitting and receiving radio signals. For example, RF front end 540 may include receiving circuitry 542 and transmitting circuitry 544. In some embodiments, receiving circuitry 542 may communicate with DL front end 560, which may include circuitry for receiving radio signals via antenna 335b.

[0098] In some implementations, switch 570 may couple transmitting circuitry 534 to uplink (UL) front-end 572. Additionally, switch 570 may couple transmitting circuitry 544 to UL front-end 572. UL front-end 572 may include circuitry for transmitting radio signals via antenna 336. Therefore, when cellular communication circuitry 530 receives an instruction to transmit according to a first RAT (e.g., supported by modem 510), switch 570 may be switched to a first state allowing modem 510 to transmit signals according to the first RAT (e.g., via a transmission chain including transmitting circuitry 534 and UL front-end 572). Similarly, when cellular communication circuitry 530 receives an instruction to transmit according to a second RAT (e.g., supported by modem 520), switch 570 may be switched to a second state allowing modem 520 to transmit signals according to the second RAT (e.g., via a transmission chain including transmitting circuitry 544 and UL front-end 572).

[0099] In some implementations, the cellular communication circuit 530 may be configured to perform methods for AI-based CSI feedback with CSI prediction, including systems, methods, and mechanisms for the UE to indicate predicted CSI reports, network configuration for CSI feedback, UE PMI report format, and AI model lifecycle management, such as in 5G NR systems, 6G systems, and above, as further described herein.

[0100] As described herein, modem 510 may include hardware and software components for implementing the features described above or for UL data used in time-division multiplexing NSA NR operation, as well as various other techniques described herein. For example, processor 512 may be configured to implement some or all of the features described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium). Alternatively (or additionally), processor 512 may be configured as a programmable hardware element (such as a FPGA (Field-Programmable Gate Array)) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or additionally), processor 512 may be configured to implement some or all of the features described herein by combining one or more of other components 530, 532, 534, 550, 570, 572, 335, and 336.

[0101] Furthermore, as described herein, processor 512 may include one or more processing elements. Therefore, processor 512 may include one or more integrated circuits (ICs) configured to perform the functions of processor 512. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 512.

[0102] As described herein, modem 520 may include hardware and software components for implementing the aforementioned features for AI-based CSI feedback with CSI prediction, including systems, methods, and mechanisms for instructing network configurations for predicted CSI reports, CSI feedback, UE PMI report formats, and AI model lifecycle management in UEs, such as in 5G NR systems, 6G systems, and above, as well as various other techniques described herein. For example, processor 522 may be configured to implement some or all of the features described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium). Alternatively (or additionally), processor 522 may be configured as a programmable hardware element (such as an FPGA (Field-Programmable Gate Array)) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or additionally), processor 522 may be configured to implement some or all of the features described herein in combination with one or more of other components 540, 542, 544, 550, 570, 572, 335, and 336.

[0103] Furthermore, as described herein, processor 522 may include one or more processing elements. Therefore, processor 522 may include one or more integrated circuits (ICs) configured to perform the functions of processor 522. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 522.

[0104] Figure 6A , Figure 6B and Figure 7 5G Core Network Architecture – Interoperability with Wi-Fi In some implementations, access to the 5G core network (CN) can be made via (or through) cellular connections / interfaces (e.g., via 3GPP communication architecture / protocols) and non-cellular connections / interfaces (e.g., non-3GPP access architecture / protocols, such as Wi-Fi connections). Figure 6A An example of a 5G network architecture according to some implementation schemes is illustrated, which combines both 3GPP (e.g., cellular) and non-3GPP (e.g., non-cellular) access to the 5G CN. As shown, a user equipment device (e.g., such as UE 106) can access the CN via both a radio access network (RAN, such as BS 604, which may be base station 102) and an access point (such as AP 612). AP 612 may include a connection to the Internet 600 and a connection to a non-3GPP interoperability function (N3IWF) 603 network entity. N3IWF may include a connection to the core access and mobility management function (AMF) 605 of the 5G CN. AMF 605 may include an instance of 5G mobility management (5G MM) function associated with UE 106. In addition, the RAN (e.g., BS 604) may also have a connection to AMF 605. Therefore, the 5G CN can support unified authentication on both connections, and allow simultaneous registration for UE 106 access via both BS 604 and AP 612. As shown in the figure, AMF 605 may include one or more functional entities associated with the 5G CN (e.g., Network Slice Selection Function (NSSF) 620, Short Message Service Function (SMSF) 622, Application Function (AF) 624, Unified Data Management (UDM) 626, Policy Control Function (PCF) 628, and / or Authentication Server Function (AUSF) 630). It should be noted that these functional entities can also be supported through the 5G CN's Session Management Functions (SMF) 606a and SMF 606b. AMF 605 can connect to (or communicate with) SMF 606a. Additionally, BS 604 can communicate with (or connect to) User Plane Function (UPF) 608a, which can also communicate with SMF 606a. Similarly, the N3IWF 603 can communicate with the UPF 608b, which in turn can communicate with the SMF 606b. Both UPFs can communicate with data networks (e.g., DN 610a and 610b) and / or the Internet 600 and the Internet Protocol (IP) Multimedia Subsystem / IP Multimedia Core Network Subsystem (IMS) Core Network 610.

[0105] Figure 6BAn example of a 5G network architecture according to some implementation schemes is illustrated, which combines both dual 3GPP (e.g., LTE and 5G NR) and non-3GPP access to the 5GCN. As shown, a user equipment device (e.g., such as UE106) can access the 5G CN via both a radio access network (RAN, such as BS 604 or eNB 602, which may be base station 102) and an access point (such as AP 612). AP 612 may include a connection to the Internet 600 and a connection to the N3IWF 603 network entity. N3IWF may include a connection to the AMF 605 of the 5G CN. AMF 605 may include an instance of 5G MM functionality associated with UE 106. In addition, the RAN (e.g., BS 604) may also have a connection to AMF 605. Therefore, the 5G CN can support unified authentication on both connections, and allow simultaneous registration for UE 106 access via both BS 604 and AP 612. Additionally, the 5GCN supports dual registration of the UE on both a legacy network (e.g., LTE via eNB 602) and a 5G network (e.g., via BS 604). As shown in the figure, the eNB 602 can have connections to both the Mobility Management Entity (MME) 642 and the Service Gateway (SGW) 644. The MME 642 can have connections to both the SGW 644 and the AMF 605. Furthermore, the SGW 644 can have connections to both the SMF 606a and the UPF 608a. As shown in the figure, the AMF 605 can include one or more functional entities associated with the 5G CN (e.g., NSSF 620, SMSF 622, AF 624, UDM 626, PCF 628, and / or AUSF 630). Note that the UDM 626 can also include Home Subscriber Server (HSS) functionality, and the PCF can also include Policy and Charging Rules (PCRF) functionality. It should also be noted that these functional entities can also be supported by the 5G CN's SMF 606a and SMF 606b. The AMF 605 can connect to (or communicate with) the SMF 606a. Additionally, the BS 604 can communicate with (or connect to) the UPF 608a, which in turn can communicate with the SMF 606a. Similarly, the N3IWF 603 can communicate with the UPF 608b, which can also communicate with the SMF 606b. Both UPFs can communicate with data networks (e.g., DN 610a and 610b) and / or the Internet 600 and the IMS core network 610.

[0106] It should be noted that, in various implementations, one or more of the network entities described above may be configured to perform methods for AI-based CSI feedback with CSI prediction, including systems, methods, and mechanisms for UEs to instruct predicted CSI reports, network configurations for CSI feedback, UE PMI report formats, and AI model lifecycle management, such as those further described herein.

[0107] Figure 7 Examples of baseband processor architectures for UEs (e.g., such as UE 106) according to some implementation schemes are illustrated. Figure 7 The baseband processor architecture 700 described herein can be implemented on one or more radio components (e.g., radio components 429 and / or 430) or modems (e.g., modems 510 and / or 520) as described above. As shown, the non-access stratum (NAS) 710 may include a 5G NAS 720 and a traditional NAS 750. The traditional NAS 750 may include a communication connection with a traditional access stratum (AS) 770. The 5G NAS 720 may include communication connections with a 5G AS 740, a non-3GPP AS 730, and a Wi-Fi AS 732. The 5G NAS 720 may include functional entities associated with both access strata. Therefore, the 5G NAS 720 may include multiple 5G MM entities 726 and 728 and 5G session management (SM) entities 722 and 724. The traditional NAS 750 may include functional entities such as Short Message Service (SMS) entity 752, Evolved Packet System (EPS) Session Management (ESM) entity 754, Session Management (SM) entity 756, EPS Mobility Management (EMM) entity 758, and Mobility Management (MM) / GPRS Mobility Management (GMM) entity 760. Additionally, the traditional AS 770 may include functional entities such as LTE AS 772, UMTS AS 774, and / or GSM / GPRS AS 776.

[0108] Therefore, the baseband processor architecture 700 allows for a common 5G-NAS for both 5G cellular and non-cellular (e.g., non-3GPP access) networks. The baseband processor architecture 700 can communicate with one or more UICC 745s. Note that, as shown in the figure, the 5G MM can maintain separate connection management and registration management state machines for each connection. Additionally, a device (e.g., UE 106) can register to a single PLMN (e.g., a 5G CN) using both 5G cellular and non-cellular access. Furthermore, a device can be in a connected state in one access and an idle state in another, or vice versa. Finally, for both accesses, there may be common 5G-MM procedures (e.g., registration, deregistration, identification, authentication, etc.).

[0109] It should be noted that, in various implementations, one or more of the functional entities of the 5G NAS and / or 5G AS described above may be configured to perform methods for AI-based CSI feedback with CSI prediction, including systems, methods, and mechanisms for the UE to instruct predicted CSI reports, network configuration for CSI feedback, UE PMI report format, and AI model lifecycle management, such as those further described herein.

[0110] Figure 8 and Figure 9 Baseband processor architecture and interface Figure 8 Example components of device 800 according to some embodiments are illustrated. In some embodiments, device 800 may include application circuitry 802, baseband circuitry 804, radio frequency (RF) circuitry 806, front-end module (FEM) circuitry 808, one or more antennas 810, and power management circuitry (PMC) 812 (at least coupled together as shown). Components of the illustrated device 800 may be included in a UE or RAN node. In some embodiments, device 800 may include fewer components (e.g., the RAN node may not utilize application circuitry 802, but instead include a processor / controller to process IP data received from the EPC). In some embodiments, device 800 may include additional components such as memory / storage devices, displays, cameras, sensors, or input / output (I / O) interfaces. In other embodiments, the components described below may be included in more than one device (e.g., the circuitry may be individually included in more than one device for a cloud RAN (C-RAN) specific implementation).

[0111] Application circuitry 802 may include one or more application processors. For example, application circuitry 802 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The one or more processors may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). These processors may be coupled to or may include memory / storage devices and may be configured to execute instructions stored in the memory / storage device to enable various applications or operating systems to run on device 800. In some embodiments, the processor of application circuitry 802 may process IP data packets received from the EPC.

[0112] Baseband circuitry 804 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. Baseband circuitry 804 may include one or more baseband processors or control logic components to process baseband signals received from the receive signal path of RF circuitry 806 and generate baseband signals for the transmit signal path of RF circuitry 806. Baseband processing circuitry 804 may interact with application circuitry 802 to generate and process baseband signals and control the operation of RF circuitry 806. For example, in some embodiments, baseband circuitry 804 may include a third-generation (3G) baseband processor 804A, a fourth-generation (4G) baseband processor 804B, a fifth-generation (5G) baseband processor 804C, or other existing, under development, or future generations of baseband processors 804D (e.g., second-generation (2G), sixth-generation (6G), etc.). Baseband circuitry 804 (e.g., one or more baseband processors among baseband processors 804A-804D) may handle various radio control functions to implement communication with one or more radio networks via RF circuitry 806. In other embodiments, some or all of the functions of the baseband processors 804A to 804D may be included in modules stored in memory 804G and executed via a central processing unit (CPU) 804E. Radio control functions may include, but are not limited to, signal modulation / demodulation, encoding / decoding, and radio frequency shifting. In some embodiments, the modulation / demodulation circuitry of the baseband circuitry 804 may include Fast Fourier Transform (FFT), precoding, or constellation mapping / demapping functions. In some embodiments, the encoding / decoding circuitry of the baseband circuitry 804 may include convolution, tail-biting convolution, turbo, Viterbi, or low-density parity-check (LDPC) encoder / decoder functions. Implementations of the modulation / demodulation and encoder / decoder functions are not limited to these examples, and other suitable functions may be included in other embodiments.

[0113] In some embodiments, the baseband circuitry 804 may include one or more audio digital signal processors (“DSPs”) 804F. The audio DSP 804F may include elements for compression / decompression and echo cancellation, and in other embodiments may include other suitable processing elements. In some embodiments, components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on the same circuit board. In some embodiments, some or all of the components of the baseband circuitry 804 and the application circuitry 802 may be implemented together, for example, on a system-on-a-chip (SOC).

[0114] In some implementations, baseband circuit 804 can provide communication compatible with one or more radio technologies. For example, in some implementations, baseband circuit 804 can support communication with the Evolved Universal Terrestrial Radio Access Network (EUTRAN) or other Wireless Metropolitan Area Networks (WMAN), Wireless Local Area Networks (WLAN), Wireless Personal Area Networks (WPAN), and direct communication with the UE. Implementations in which baseband circuit 804 is configured to support radio communication with more than one radio protocol may be referred to as multi-mode baseband circuits.

[0115] RF circuit 806 enables communication with a wireless network via a non-solid medium using modulated electromagnetic radiation. In various embodiments, RF circuit 806 may include switches, filters, amplifiers, etc., to facilitate communication with the wireless network. RF circuit 806 may include a receive signal path, which may include circuitry for down-converting the RF signal received from FEM circuit 808 and providing a baseband signal to baseband circuit 804. RF circuit 806 may also include a transmit signal path, which may include circuitry for up-converting the baseband signal provided by baseband circuit 804 and providing an RF output signal for transmission to FEM circuit 808.

[0116] In some embodiments, the receive signal path of RF circuit 806 may include mixer circuit 806A, amplifier circuit 806B, and filter circuit 806C. In some embodiments, the transmit signal path of RF circuit 806 may include filter circuit 806C and mixer circuit 806A. RF circuit 806 may also include synthesizer circuit 806D for synthesizing the frequency used by mixer circuit 806A in both the receive and transmit signal paths. In some embodiments, mixer circuit 806A in the receive signal path may be configured to down-convert the RF signal received from FEM circuit 808 based on the synthesized frequency provided by synthesizer circuit 806D. Amplifier circuit 806B may be configured to amplify the down-converted signal, and filter circuit 806C may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signal to generate an output baseband signal. The output baseband signal may be provided to baseband circuit 804 for further processing. In some implementations, these output baseband signals may be zero-frequency baseband signals, but this is not necessary. In some implementations, the mixer circuit 806A in the receive signal path may include a passive mixer, but the scope of the implementations is not limited in this respect.

[0117] In some implementations, the mixer circuit 806A of the transmit signal path can be configured to up-convert the input baseband signal based on the synthesized frequency provided by the synthesizer circuit 806D to generate an RF output signal for the FEM circuit 808. The baseband signal can be provided by the baseband circuit 804 and can be filtered by the filter circuit 806C.

[0118] In some embodiments, the mixer circuit 806A for the receive signal path and the mixer circuit 806A for the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. In some embodiments, the mixer circuit 806A for the receive signal path and the mixer circuit 806A for the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection). In some embodiments, the mixer circuit 806A for the receive signal path and the mixer circuit 806A for the transmit signal path may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuit 806A for the receive signal path and the mixer circuit 806A for the transmit signal path may be configured for superheterodyne operation.

[0119] In some embodiments, the output baseband signal and the input baseband signal may be analog baseband signals, but the scope of the embodiments is not limited in this respect. In some alternative embodiments, the output baseband signal and the input baseband signal may be digital baseband signals. In these alternative embodiments, RF circuit 806 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry, and baseband circuit 804 may include a digital baseband interface for communicating with RF circuit 806.

[0120] In some dual-mode implementations, separate radio IC circuits may be provided to process signals for each spectrum, but the scope of the implementation is not limited in this respect.

[0121] In some implementations, synthesizer circuit 806D may be a fractional-N synthesizer or a fractional-N / N+1 synthesizer, but the scope of implementations is not limited in this respect, as other types of frequency synthesizers may also be suitable. For example, synthesizer circuit 806D may be a Δ-∑ synthesizer, a frequency multiplier, or a synthesizer including a phase-locked loop with a frequency divider.

[0122] Synthesizer circuit 806D can be configured to synthesize an output frequency based on the frequency input and the divider control input for use by mixer circuit 806A of RF circuit 806. In some embodiments, synthesizer circuit 806D may be a fractional N / N+1 synthesizer.

[0123] In some implementations, the frequency input may be provided by a voltage-controlled oscillator (VCO), although this is not mandatory. The divider control input may be provided by the baseband circuitry 804 or the application processor 802 according to the desired output frequency. In some implementations, the divider control input (e.g., N) may be determined from a lookup table based on the channel indicated by the application processor 802.

[0124] The synthesizer circuit 806D of the RF circuit 806 may include a frequency divider, a delay-locked loop (DLL), a multiplexer, and a phase accumulator. In some embodiments, the frequency divider may be a dual-mode divider (DMD), and the phase accumulator may be a digital phase accumulator (DPA). In some embodiments, the DMD may be configured to divide the input signal by N or N+1 (e.g., based on carry) to provide a fractional division ratio. In some example embodiments, the DLL may include a cascaded, tunable delay element, a phase detector, a charge pump, and a set of D-type flip-flops. In these embodiments, the delay elements may be configured to divide the VCO cycle into Nd equal phase groups, where Nd is the number of delay elements in the delay line. Thus, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

[0125] In some embodiments, synthesizer circuitry 806D may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and frequency divider circuitry to generate multiple signals having multiple different phases relative to each other at the carrier frequency. In some embodiments, the output frequency may be the LO frequency (fLO). In some embodiments, RF circuitry 806 may include an IQ / polarity converter.

[0126] FEM circuit 808 may include a receive signal path, which may include circuitry configured to operate on RF signals received from one or more antennas 810, amplify the received signals, and provide an amplified version of the received signals to RF circuit 806 for further processing. FEM circuit 808 may also include a transmit signal path, which may include circuitry configured to amplify a transmit signal provided by RF circuit 806 for transmission by one or more of the one or more antennas 810. In various embodiments, amplification via the transmit or receive signal path may be performed only in RF circuit 806, only in FEM 808, or in both RF circuit 806 and FEM 808.

[0127] In some embodiments, FEM circuit 808 may include a TX / RX switch for switching between transmit and receive mode operation. The FEM circuit may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuit may include an LNA for amplifying the received RF signal and providing the amplified received RF signal as an output (e.g., provided to RF circuit 806). The transmit signal path of FEM circuit 808 may include a power amplifier (PA) for amplifying (e.g., provided by RF circuit 806) the input RF signal; and one or more filters for generating an RF signal for subsequent transmission (e.g., through one or more antennas in one or more antennas 810).

[0128] In some implementations, the PMC 812 manages the power supplied to the baseband circuitry 804. Specifically, the PMC 812 controls power selection, voltage scaling, battery charging, or DC-DC conversion. The PMC 812 is typically included when the device 800 can be battery powered, for example, when the device is included in a UE. The PMC 812 can improve power conversion efficiency while providing the desired specific implementation size and thermal characteristics.

[0129] Although Figure 8A PMC 812 is shown coupled only to the baseband circuit 804. However, in other embodiments, the PMC 812 may be additionally or alternatively coupled to other components, such as, but not limited to, application circuit 802, RF circuit 806, or FEM 808, and perform similar power management operations.

[0130] In some implementations, PMC 812 may be controlled or otherwise incorporated into various power-saving mechanisms of device 800. For example, if device 800 is in RRC_connected state, where the device is still connected to the RAN node because it expects to receive traffic immediately, the device may enter a state known as discontinuous receive mode (DRX) after a period of inactivity. During this state, device 800 may be powered down for short intervals, thereby saving power.

[0131] If there is no data traffic activity during the extended period, device 800 can transition to the RRC_Idle state, in which the device disconnects from the network and does not perform operations such as channel quality feedback or handover. Device 800 enters a very low power state and performs paging, in which the device periodically wakes up again to listen to the network, and then powers off again. Device 800 cannot receive data in this state. To receive data, the device can transition back to the RRC_Connected state.

[0132] An additional power-saving mode renders the device unusable for a period exceeding the paging interval (from seconds to hours). During this time, the device is completely unconnected to the network and may be completely powered off. Any data transmitted during this period will incur significant latency, which is assumed to be acceptable.

[0133] The processor of application circuit 802 and the processor of baseband circuit 804 can be used to execute elements of one or more instances of the protocol stack. For example, the processor of baseband circuit 804 can be used individually or in combination to perform layer 3, layer 2, or layer 1 functions, while the processor of application circuit 804 can utilize data received from these layers (e.g., packet data) and further perform layer 4 functions (e.g., transmit communication protocol (TCP) and user datagram protocol (UDP) layers). As mentioned herein, layer 3 may include the radio resource control (RRC) layer, which will be described in further detail below. As mentioned herein, layer 2 may include the media access control (MAC) layer, the radio link control (RLC) layer, and the packet data convergence protocol (PDCP) layer, which will be described in further detail below. As mentioned herein, layer 1 may include the physical (PHY) layer of the UE / RAN node, which will be described in further detail below.

[0134] Figure 9 An exemplary interface of a baseband circuit according to some embodiments is shown. As discussed above, Figure 8 The baseband circuit 804 may include processors 804A-804E and a memory 804G utilized by the processors. Each of the processors 804A-804E may respectively include a memory interface 904A-904E for sending / receiving data to / from the memory 804G.

[0135] The baseband circuit 804 may also include one or more interfaces for communicatively coupling to other circuits / devices, such as a memory interface 912 (e.g., an interface for transferring / receiving data to / from a memory external to the baseband circuit 804) or an application circuit interface 914 (e.g., an interface for transferring / receiving data to / from a memory external to the baseband circuit 804). Figure 8 Application circuit 802 is an interface for transmitting / receiving data), and RF circuit interface 916 (e.g., for sending / receiving data to / from...). Figure 8 The RF circuit 806 is an interface for transmitting / receiving data, and the wireless hardware connection interface 918 is used for transmitting / receiving data to / from near field communication (NFC) components, Bluetooth, etc. ® Components (e.g., Bluetooth) ® Low power consumption, Wi-Fi ® Interfaces for transmitting / receiving data to / from components and other communication components) and power management interface 920 (e.g., an interface for transmitting / receiving power or control signals to / from PMC 812).

[0136] Figure 10 Network architecture supporting AI / ML Figure 10 An example architecture of an artificial intelligence (AI)-enabled system 1000, including an open radio access network (O-RAN), is illustrated according to some implementation schemes. System 1000 includes a radio access network (RAN) 1002 and a core network 1004 interconnected via logical interfaces.

[0137] RAN 1002 provides radio connectivity between user equipment (UE) (e.g., UE 106) and core network 1004. RAN 1004 may be provided by BS 102, which interfaces with UE 106 via a radio interface to provide connectivity and mobility management.

[0138] RAN 1002 may include a distributed unit (DU) 104 that hosts lower-level radio interface protocols including PDCP and RLC.

[0139] RAN 1002 may include a centralized unit (CU) that hosts higher-level radio protocols. The CU is split into a control plane (CU-CP) 1012 that hosts Radio Resource Control (RRC) message transmission and reception, and a user plane (CU-CP) 1014 that hosts the Service Data Adaptation Protocol (SDAP) layer and the Packet Data Convergence Protocol (PDCP) layer. CU-UP 1012 also performs AI / ML model training and inference.

[0140] RAN 1002 may include a non-real-time RAN Intelligent Controller (RIC) 1008 (which may be a software entity, computer system, and / or server, such as server 104) and a near real-time RIC 1010, as well as various other functions and / or entities. The non-real-time RIC 1008 may control functions at intervals greater than 1 second, while the near real-time (near RT) RIC 1010 may control RAN functions at intervals less than 1 second. In some embodiments, trained models and real-time control functions generated in the non-real-time RIC 1008 may be distributed to the near real-time RIC 1010 for runtime execution. In other words, in some embodiments, the non-real-time RIC 1008 may be policy-guided logical functions that enable non-real-time control and optimization of RAN elements and resources, AI / ML workflows (including model training and updates), and applications / features in the near real-time RIC 1010.

[0141] The core network 1004 may include an Access and Mobility Management Function (AMF) 1032 for access control and mobility management, a User Plane Function (UPF) 1034 for forwarding user data, a Unified Data Management (UDM) 1046 for managing subscriber data, policies and / or authorizations, a Session Management Function (SMF) 1036 for session establishment and modification, a Data Collection and Integration Function (DCCF) 1044 for aggregating data from UE 106 and RAN 1002 for AI / ML model training, an AI Data Repository Function (ADRF) 1042 for storing aggregated data for AI / ML, a Network Data Analysis Function (NWDAF) 1050 for performing analysis on the aggregated data, and a Transcoding Entity (TCE) 1048 for media transcoding.

[0142] An "over-the-top" (OTT) server 1060 is also described, which is associated with UPF 1034 and CU-UP1014 and can provide applications and services directly to UE 106 via the Internet.

[0143] Figures 11A to 11C AI / ML Edge User Gear Diagram Figures 11A to 11BAn example of an AI / ML edge user equipment (AEU) 106b deployed to enhance AI / ML performance using sidelink connectivity is illustrated. In future wireless networks such as sixth-generation cellular networks, AEUs (e.g., AEU 106B) are a type of user equipment (UE) referred to as AI / ML edge UEs (AEUs) designed to improve AI / ML performance within the cellular network. The AEU 106B can be a dedicated new UE with AI / ML capabilities or existing network equipment such as, for example, a repeater, an Integrated Access and Backhaul (IAB) node, or a reconfigurable and intelligent surface (RIS) node enhanced with AI / ML functionality. The AEU 106B can collect raw data from nearby out-of-coverage (OOC) UEs and report the data to the network (e.g., base station 102). The AEU 106B can forward AI / ML models from the network to the OOC UEs to enable cooperative network-UE AI / ML. The AEU 106B can use data from nearby OOC UEs and in-coverage (IC) UEs to perform localized training and inference, and then report the results to the network. Additionally, the AEU 106B can support multi-vendor AI / ML training and inference for nearby UEs with different radio components but no cellular radio components (e.g., UEs with wireless LAN (WLAN) connections such as IEEE 802.11 Wi-Fi direct connections).

[0144] Now transferred to Figure 11A , Figure 11A A diagram depicts a system 1100 including an AI / ML edge UE (AEU) 106B, which establishes a connection to a base station 102, which provides access to a core network (e.g., Figure 10 The connectivity of AEU 106B (1004 in the example). AEU 106B can establish one or more peer connections with multiple user equipment (UEs) such as, for example, out-of-coverage (OOC) UE1 (e.g., UE 106D), OOC UE2 (e.g., UE 106C) and one or more in-coverage UEs such as UE 1 (e.g., UE 106A)).

[0145] As used herein, out-of-coverage refers to a UE that is not connected to a base station such as base station 102. Due to transmit power limitations (e.g., the UE is located outside the range of the BS), an OOC UE may not be able to connect to BS 102. However, an OOC UE may also be unable to connect to the BS. For example, an OOC UE may be configured to operate with a different mobile network operator (MNO), or may not have cellular radio components capable of connecting to BS 102.

[0146] The AEU 106B can have a 6G RAN connection to BS 102 for control signaling, and a peer-to-peer connection to nearby UEs (e.g., UEs 106C-106D) using various technologies such as 5G PC5, Wi-Fi, Bluetooth, etc. The AEU can be used to enhance the use of AI / ML near the edge of the wireless cellular network. The AEU 106B can discover nearby UEs and be discovered by nearby UEs to determine whether the AEU 106B can assist edge AI / ML processing. A new multi-radio multi-vendor discovery process and AI / ML-aware AEU selection are introduced. New messages and processes between the AEU 106B and base station 102 enable the exchange of data, models, and training / inference results to support collaborative AI / ML with UEs. Therefore, the AEU 106B extends AI / ML capabilities to edge devices and amplifies network-based AI / ML through intelligent sidelink utilization.

[0147] Additionally, as used herein, peering connection can refer to a direct wireless connection between two user equipments that does not require traversal of base station 102. Examples of peering connections include direct WLAN connections or other types of connections that utilize short-range wireless technologies such as sidelink connections, 5G PC5 connections, F1 interfaces, Wi-Fi connections, backhaul links, or Bluetooth connections to establish direct links between the AEU and other UEs for collaborative AI / ML. Sidelink connections are device-to-device (D2D) communication technologies developed by 3GPP. 5G PC5 is cellular vehicle-to-everything connectivity. F1 interfaces are typically used to connect a BS CU to a BS DU via F1. These examples are not intended to be limiting. A variety of different types of peering wireless connections can be used to enable the AEU 106B to communicate with nearby OOC UE 106D and IC UE 106A. Peering connections allow the AEU 106B to communicate with UEs outside the coverage area of ​​base station 102.

[0148] It should be noted that current L2 / L3 sidelink relay user equipment (UEs) only forward data and are unaware of the actual content. Therefore, these UEs cannot support the local / edge AI / ML training and inference operations proposed for AEU 106B. In contrast, AEU 106B can use data from surrounding UEs to perform model training and inference at the edge. AEU 106B can select appropriate UEs for model training and transfer learning. AEU 106B can also establish data / model exchange configurations between OOC UEs and in-coverage (IC) UEs based on network input. This localized edge AI / ML approach of AEU 106B alleviates mobile phone limitations and captures unique environmental observations.

[0149] Therefore, the AEU 106B can establish various peering connections with nearby User Equipment (UE). Different peering communication options provide flexibility and support connectivity with the AEU 106B for different use cases. These options may include F1 links similar to Integrated Access and Backhaul (IAB), which can be used for backhaul and control. The AEU 106B can establish Wi-Fi connections to allow it to utilize existing Wi-Fi networks and capabilities. It can also establish Bluetooth connections to provide a short-range option for connecting to nearby devices. The AEU 106B can act as their backhaul via peering offload tasks through reconfigurable Smart Surfaces (RIS). Furthermore, the AEU 106B can establish Near Field Communication (NFC) and Ultra Wideband (UWB) connections to allow proximity-based interaction.

[0150] Figure 11B The AEU 106B is described as collecting raw data from one or more out-of-coverage UEs (e.g., UEs 106C and 106D) via one or more WLAN (e.g., peer-to-peer) connections (such as Wi-Fi Direct or another type of P2P connection). In one aspect, the AEU 106B collects raw data from OOC UE 106D via a sidelink connection and from OOC UE 106C via one or more different sidelink connections. The AEU 106B can aggregate and analyze the raw data from multiple UEs and send the results to the core network via a connection to base station 102. This allows the AEU 106B to collect useful data from UEs that the network cannot directly connect to.

[0151] In this manner, sidelinks allow the collection of raw data from user equipment (UEs) located outside network coverage (OOC). Since OOC UEs cannot directly report the collected data to the network, peering connections to AEU 106B provide an alternative path. Peering connections extend network-UE AI / ML collaboration to OOC UEs that could previously only perform on-device AI / ML when disconnected from the cellular network.

[0152] Additionally, peer-to-peer connections such as sidelinks enable the AEU 106B to perform local training and inference at the edge of base station 102 via sidelinks, and processing can be offloaded from the network. Furthermore, edge sidelink training alleviates privacy concerns regarding the UE sharing raw data with the network, as the data can remain local to the AEU.

[0153] Peer connectivity enables AEU 106B AI / ML collaboration between UEs with different radio components and vendors. For example, one UE (e.g., UE 106D) can use 5G PC5, while another UE (e.g., UE 106C) can be configured to use Wi-Fi radio components. Alternatively, UE 106D and UE 106C may have cellular radio components configured for different MNOs (such as those from vendor A and vendor B), or they may be UEs manufactured by different vendors (such as Apple or Google). Peer connectivity enables AEU 106B to provide multi-vendor AI / ML capabilities.

[0154] In one example, such as Figure 11C As depicted, for privacy reasons, UEs 106A, 106C, and 106D may not disclose certain information, such as identification data (ID) shared with BS 102 and associated data collection. Therefore, prior to data collection, BS 102 may transmit a measurement configuration to AEU 106B specifying the data types required for training, without including any UE IDs, such as anonymized location or area activity information. AEU 106B may include information about UEs (e.g., UE 106A, UE 106C, and / or UE 106D) that this data type will be collected in peer-to-peer broadcasts to discover UEs that can provide the desired data. AEU 106B may establish a peer-to-peer link with the responding UE, collect the specified data, and transmit the aggregated / anonymized results to BS 102 without any UE IDs. If model training occurs at BS 102, AEU 106B can relay the trained model to the corresponding UEs without revealing the identities of these UEs. This allows for the collection of privacy-preserving data from UEs for AI / ML purposes.

[0155] Figure 12 Signaling for 6G AI / ML network enhancement Figure 12 An example of signaling 1200 for implementing 6G AI / ML network enhancements utilizing peer-to-peer connections is illustrated. That is, Figure 12 Example signaling is illustrated for implementing AI / ML model transfer to an out-of-coverage user equipment (OOC UE) 106A via secondary connectivity.

[0156] Figure 12 The signaling 1200 shown can be used in conjunction with any of the systems, methods, and / or devices. In various embodiments, some of the signaling shown may be executed concurrently in a different order than that shown, or may be omitted. Additional signaling may also be executed as needed. As shown in the figure, the signaling can be implemented in one of the following example embodiments.

[0157] At 1210, signaling can begin the initial registration or mobility process between the UE (e.g., 106A) and the core network / RAN 1000.

[0158] In step 1220, the core network / RAN 1000 may respond with a registration acceptance message that provides configuration information for the AI / ML server. This configuration information includes parameters such as, for example, the Internet Protocol (IP) address and security credentials used to connect to the AI / ML server 1202.

[0159] In step 1230, OOC UE 106A transmits a registration completion message and stores AI / ML server configuration information for future use. Therefore, as in step 1220, when OOC UE 106A subsequently loses coverage from the core network / RAN 1000, OOC UE 106A can establish secondary connectivity to the network AI / ML server 1202 based on the stored configuration, as in step 1230. This secondary connectivity can be via a peer-to-peer connection (such as a WLAN connection), or, if the device has dual subscriber identity module (SIM) capabilities, it uses a second SIM.

[0160] Later in step 1240, when OOC UE 106A needs to share AI / ML data but does not have coverage with the core network / RAN1000, OOC UE 106A may establish secondary connectivity to AI / ML server 1202 based on the stored configuration using a WLAN connection (such as those previously discussed) or a second SIM, as in step 1250.

[0161] In step 1260, when outside the coverage of the core network / RAN 1000, the OOC UE 106A can utilize secondary connectivity to exchange AI / ML data with the network AI / ML server 1240. Therefore, when outside the coverage of the core network, the OOC UE 106A utilizes secondary connectivity to exchange AI / ML data, models, training results, or inferences with the network AI / ML server 1240. This allows the OOC UE 106A to remotely collaborate on AI / ML tasks via secondary connectivity until the OOC UE regains coverage with the core network / RAN 1000.

[0162] Figure 13 Timing diagram of AEU control signaling Figure 13 Example timing diagram signaling 1300 is illustrated according to some implementation schemes between user equipment (UE), AI / ML edge user equipment (AEU), and BS for supporting 6G AI / ML network enhancements utilizing peer-to-peer connections. Figure 13The signaling 1300 shown can be used in conjunction with any of the systems, methods, and / or devices. In various embodiments, some of the signaling shown may be executed concurrently in a different order than that shown, or may be omitted. This example uses an OOC UE, but this is not intended to be limiting. In some embodiments, the IC UE may also communicate with the AEU. Additional signaling may also be executed as needed. As shown, the signaling can follow the following flow.

[0163] Figure 13 An example signaling flow for supporting AI / ML enhancements using peer-to-peer communication is illustrated between the OOC User Equipment (UE), AI / ML Edge User Equipment (AEU), and BS 102.

[0164] At 1310, signaling can begin with AEU performing discovery and establishing a PC5 connection or another desired type of peer connection with OOC UE2.

[0165] In step 1320, the AEU establishes a peer connection (e.g., a Wi-Fi connection) with the OOC UE1 after discovery.

[0166] In step 1330, the AEU transmits a first control message (e.g., New Control Message 1) to the BS, indicating the capabilities, coverage, and willingness to share data of the UEs connected to the AEU (e.g., OOC UE 1 and / or OOO UE 2). OOC UEs may be pre-configured to be willing or unwilling to share certain types of raw data usable by AI / ML. Alternatively, the user may be prompted during the discovery process regarding their desire to share data.

[0167] In step 1340, the BS transmits a second control message (e.g., new control message 2) to the AEU. This second control message contains measurement and reporting configurations for one or more UEs (e.g., OOC UE 1 and / or OOO UE 2), and the second control message may be included in a container. The container may be an additional container 1350 for containing the second control message and may include UE configurations.

[0168] In step 1360, the AEU forwards the measurement and reporting configuration to OOO UE 2 via a peer connection (e.g., PC5).

[0169] In step 1370, the AEU forwards the measurement and reporting configuration to OOO UE 1 via a peer connection (e.g., Wi-Fi).

[0170] In step 1380, UE 2 (OOO) transmits raw data to the AEU via a peer connection (e.g., PC5). In step 1385, UE 1 (OOO) transmits raw data to the AEU via a side link (e.g., a Wi-Fi side link). The AEU can aggregate the raw data received from the UE. In step 1390, the AEU transmits the aggregated raw data to the BS in a new control message (e.g., a third control message).

[0171] Figure 14 : A flowchart for local training / inference offloading for AI / ML Figure 14 An example of an AEU system 1400 is illustrated, which uses data from nearby out-of-coverage (OOC) and in-coverage (IC) user equipment to perform local training and inference to offload AI / ML from the network. In one example, AEU 106B can use data from UEs 106A, 106C, and 106D to perform AI / ML training and inference locally. This offloads processing work from the network. AEU 106B can perform localized training and inference of AI / ML models and reduces privacy issues associated with UEs transmitting raw data across the network. AEU 106B can have greater computing power than the UEs used to perform training / inference, and individual UEs (e.g., UEs 106A, 106C, and 106D) may lack robust AI / ML support. Training on related data by different UEs can produce redundant results. AEU 106B can fuse results by training / inference on aggregated data from multiple UEs 106A, 106C, and 106D. Therefore, AEU 106B can leverage its edge location and peer connections to UEs 106A, 106C, and 106D to intelligently perform localized AI / ML processing for training and inference of AI / ML models. This achieves benefits such as reduced network load, better privacy, and improved AI / ML model accuracy. It should be noted that, by way of example only, AEU 106 could be an AEU deployed at an intersection to train an AI / ML model for each intersection (e.g., the AEU could collect all informational data related to the intersection (e.g., traffic) and use the collected data to train an AI model to improve peer performance).

[0172] To further illustrate, in one implementation, the AEU 106B can coordinate collaborative edge-based AI / ML training and inference between the BS 102 and connected UEs 106A, 106C, and 106D via peering connections between the AEU 106B and one or more UEs 106A, 106C, and 106D. Peering connections can be of the same type or different types, as discussed herein. The AEU 106B can establish one or more peering connections with nearby out-of-coverage UEs 106C-106D and in-coverage UEs 106A via a discovery process.

[0173] BS 102 can configure AEU 106B to report information about the UEs connected to the AEU, including the coverage status of these UEs and their willingness to perform local AI / ML processing. Based on the AEU reports, BS 102 can use alternative methods (such as transparent containers in L1 / L2 or L3 signaling, for example, RRC signaling from an edge server) to transmit AI / ML models to AEU 106B.

[0174] The AEU 106B can forward AI / ML models received from the BS 102 to each connected UE 106A, 106C, and 106D within a transparent container in the sidelink signaling. Each UE 106A, 106C, and 106D can perform local training on the AI ​​model and report the training results back to the AEU 106B within the peer signaling transparent container. The AEU 106B can aggregate the local training results from the UEs and transmit the fused model updates to the BS 102.

[0175] According to one implementation, new RRC-based control messages are defined to implement signaling for AI / ML coordination between the BS and AEU (e.g., from AEU to BS). These new control messages may alternatively be implemented using other Layer 2 or Layer 3 (L2 / L3) protocols. The first new message from AEU to BS (e.g., Figure 13 The new control message 1) can be used to indicate one or more of the capabilities of a UE connected to the AEU, including: UE identifier, UE coverage status (in-coverage or out-of-coverage), peer connection type (e.g., PC5, Wi-Fi, BT, etc.), indication of willingness to share data, or indication of willingness to cooperate with UE-network AI / ML.

[0176] The second new message from BS to AEU (e.g., Figure 13 The new control message 2) may contain one or more configuration information from the configuration information of the connected UE, including: a container with UE-specific data collection and reporting configuration and a container with an AI / ML model.

[0177] The third new message from AEU to BS (e.g., Figure 13 The new control message 3) can be used to report data and AI / ML results, including: one or more of the following: containers with raw data collected per UE, containers with training and inference results per UE, or containers with aggregated / fused training and inference results. The new RRC-based control message signaling enables the AEU to coordinate AI / ML processing at the edge by interfacing between the BS and the UE connected to the sidelink.

[0178] Figure 15 Timing diagram of AEU control signaling used for AI / ML model training Figure 15 A timing diagram signaling 1500 illustrates an example of an AEU selection process that supports AI / ML network enhancements using peer-to-peer connectivity, according to some implementation schemes. In other words, timing diagram 1500 depicts the process for selecting an AEU that supports AI / ML network enhancements using peer-to-peer connectivity. Figure 15 The signaling 1500 shown can be used in conjunction with any of the systems, methods, and / or devices. In various embodiments, some of the signaling shown may be executed concurrently in a different order than that shown, or may be omitted. Additional signaling may also be executed as needed. As shown in the figure, the signaling can follow the following flow.

[0179] At 1510, signaling may begin when the AEU performs discovery via a peer connection (e.g., PC5) and establishes a bidirectional PC5 connection with the target UE (e.g., OOC UE2).

[0180] At 1520, the AEU can perform discovery via a peer-to-peer connection (e.g., Wi-Fi) and establish a bidirectional Wi-Fi connection with an additional target UE (e.g., OOC UE1).

[0181] At step 1530, the AEU may transmit a first new control message (e.g., new control message 1) to the BS, which indicates the capabilities, coverage, and willingness for local training of the connected OOC UEs (OOC UE1 and OOC UE2).

[0182] At step 1540, the BS may transmit a second new control message (e.g., new control message 2) to the AEU, which contains an AI / ML model for each connected OOC UE (OOC UE 1 and OOC UE 2) within a container. The container may be an additional container 1550 for containing the second control message and includes UE configuration.

[0183] At step 1560, the AEU can forward the AI / ML model for OOC UE 2 to OOC UE 2 via the PC5 connection.

[0184] At step 1570, the AEU can forward the AI / ML model for OOC UE1 to OOC UE1 via Wi-Fi connection.

[0185] At step 1580, OOC UE 2 can perform local training on the AI / ML model received in step 1560 and transmit the training results back to AEU via PC5 connection.

[0186] At step 1585, OOC UE 1 can perform local training on the AI / ML model received in step 1570 and transmit the training results to AEU via Wi-Fi connection.

[0187] At step 1590, the AEU can aggregate the local training results from OOC UE 1 and OOC UE 2, and transmit the aggregated local OOC UE training results to the BS in a third new control message (e.g., new control message 3). At step 1595, the BS can fuse the training results.

[0188] Figure 16 General discovery message used for multiple peer links Figure 16 An example of a discovery message 1600 for multi-radio, multi-vendor AI / ML perception discovery according to some implementation schemes is shown.

[0189] The AEU 106B's ability to connect to multiple UEs, such as OOC UEs, using different types of peering communication can complicate signaling between the AEU and different UEs. In one example, a container can be used to enable the AEU to send discovery information via different types of peering data links with nearby UEs. The UE can use this discovery information to select the AEU.

[0190] In one example, discovery message 1600 may be specified as an Extensible Markup Language (XML) file or a similar radio-independent file format. Discovery message 1600 may be transmitted within a container within the payload of a packet transmitted over different radio links, such as, for example, PC5, Wi-Fi, etc. In one implementation, the discovery message itself may be decoupled or separate from the radio technology used to transmit it. For example, the same discovery message may be transmitted over both PC5 and Wi-Fi, instead of having two separate radio-specific discovery messages. Thus, a radio-independent discovery message containing key discovery information but not tied to any particular radio technology is provided. Discovery messages (e.g., radio-independent discovery messages) can then be transmitted over different radios, such as, for example, PC5, Wi-Fi, etc., without alteration. The discovery message is decoupled or separate from the underlying radio used.

[0191] In one example, the packet header 1604 includes a bitmap indicating whether the payload contains a discovery message, and if so, where that discovery message is located. For example, the bitmap indicates the payload of a PC5 packet in a first container 1604 of the discovery message and the payload of a Wi-Fi packet in a second container 1606 of the discovery message.

[0192] The discovery message 1600 itself may include information such as: 1) whether the UE supports data forwarding, 2) whether the UE supports AI / ML model training offload (including supported models, features, formats, etc.), 3) whether the UE supports AI / ML model inference offload (including supported models, features, formats, etc.), and 4) any remaining available AI / ML computing resources, such as, for example, floating-point operations per second (FLOPs). This allows the discovery message to exchange radio-independent AI / ML capabilities to enable multi-radio, multi-vendor discovery between devices such as AEUs and UEs.

[0193] Figure 17 A flowchart of the AEU discovery process In one implementation, the UE can be configured to select an AEU. The UE can select an AEU based on several considerations, including the radio link quality with the AEU, the processing capacity of the AEU, the amount of spare processing power of the AEU, and the signal strength between the AEU and the BS. Figure 17 Examples of methods 1700 for enabling a UE to perform an AEU selection process to support multi-radio, multi-vendor AI / ML perception discovery, according to some implementation schemes, are illustrated. Figure 17 The method 1700 illustrated in the example can be used in conjunction with any of the systems, methods, or devices shown in the figure, as well as other devices. In various embodiments, some of the method elements shown may be executed concurrently in a different order than shown, or may be omitted. Additional method elements may also be executed as needed. As shown, the method can operate as follows.

[0194] In one implementation, method 1700 describes a process for selecting an AEU to support multi-radio, multi-vendor AI / ML perception discovery. In this manner, selecting an AEU prevents greedy UE behavior that could overload a single AEU, while also supporting radio selection for coverage and load balancing tradeoffs.

[0195] In this example, method 1700 may begin at 1702, where the UE may attempt to find and / or locate one or more available AEUs, as in step 1704. At step 1706, the UE may identify the discovery bitmap of the AEU and decode the discovery information, as previously discussed. Figure 16The discussion continues. At step 1708, the UE can match the discovered AEU capability with its own requirements and measure a reference signal, such as the AEU's Received Signal Received Power (RSRP). At step 1710, the UE can determine whether the AEU's RSRP exceeds a configured RSRP threshold radio offset. If not, method 1700 returns to step 1704, where the UE can search for another AEU with the desired attributes. At step 1712, if the AEU's RSRP exceeds the configured RSRP threshold radio offset, the UE can select the AEU radio. At step 1714, the UE can determine whether the AEU's remaining AI / ML FLOP is greater than the desired FLOP. If yes, the UE can select that AEU. If no, method 1700 returns to step 1704, and the UE can search for another AEU with the desired attributes. At step 1716, the method ends.

[0196] In one implementation scheme, such as Figure 17 As described, the network can pre-configure the RSRP offset value for each radio as a baseline for comparison. This takes into account differences in radio coverage and network preferences. The UE can search for potential AEU candidates and evaluate their AI / ML capabilities and remaining computing resources. The UE can match the AEU capabilities to its own requirements. The UE can measure the RSRP of each potential AEU. If the AEU's RSRP exceeds a pre-configured RSRP threshold minus the radio-specific offset, the UE can select that AEU. This ensures sufficient coverage. Based on the AEU radio meeting the RSRP criterion, the UE can select an AEU with a remaining FLOP greater than the desired FLOP. This can be used to ensure the availability of computing resources. Finally, if multiple suitable AEUs are found, the UE can select the AEU with the largest remaining FLOP or the first suitable AEU. In summary, this example can be used to enable the UE to carefully evaluate AUEs based on coverage, load balancing, and resources to prevent any AUE from being overloaded and to ensure AI / ML performance. The network can influence the selection via RSRP offset.

[0197] Figure 18 AEU selection process Figure 18 Example procedures are provided for performing AEU selection from an AEU perspective to support multi-radio, multi-vendor AI / ML perception discovery, based on some implementation schemes. Figure 18 The method 1800 illustrated in the example can be used in conjunction with any of the systems, methods, or devices shown in the figure, as well as other devices. In various embodiments, some of the method elements shown may be executed concurrently in a different order than shown, or may be omitted. Additional method elements may also be executed as needed. As shown, the method can operate as follows.

[0198] Method 1800 begins at 1802. In this example, the UE may transmit a request message for one or more potential AEU candidates, as in step 1804.

[0199] At step 1806, the AEU may receive a message from the UE, identify the UE's discovery bitmap, and decode the information to determine the UE's capabilities.

[0200] At step 1808, the AEU can assess the UE's capabilities and compare these capabilities with the AEU's own policies, AI / ML capabilities, and measure whether the AEU can provide the services expected by the UE.

[0201] At step 1810, the AEU can determine whether the UE's RSRP exceeds the configured RSRP threshold radio offset. If not, method 1800 returns to step 1804, at which point the UE can transmit another request message.

[0202] At step 1812, the AEU may select the UE radio with the strongest RSRP from the set of eligible UEs. At step 1814, method 1800 may end.

[0203] Therefore, as Figure 18 As described, in one implementation, a novel process is provided for selecting an AEU (Autonomous Equipment Request) for AI / ML offloading. Based on network policies, conditions, and AEU computational availability, the AEU evaluates user equipment (UE) request messages and selects an appropriate UE for offloading. The UE may transmit a request including AI / ML model details and capability discovery information (e.g., AI / ML model ID, input / output characteristics, model format, etc.). The UE may transmit a unified radio-independent discovery message, such as... Figure 16 As described.

[0204] The AEU can grant permission for UE to be used for offloading based on various conditions, such as the UE's RSRP exceeding a configured threshold offset, the AEU supporting the requested model and features, and the AEU having sufficient FLOPs for the AI ​​model.

[0205] If multiple AEUs meet various conditions for an offload request to a UE, two options may be available: 1) allow the AEU to notify the network which UEs meet the criteria, and the network can configure specific AEU-UE pairings; or 2) allow the UE to select its preferred AEU and notify the AEU of its selection. This standardization process allows the AEU to selectively select UEs for offload based on network bootstrapping, AEU resources, and UE needs, in order to optimize overall AI / ML performance.

[0206] Figure 19 Methods for providing enhanced AI / ML performance Figure 19 Examples of methods 1900 for providing enhanced AI / ML performance using peer-to-peer connections with AI / ML edge user equipment (AEU) are illustrated according to some implementation schemes. Figure 19 The method 1900 illustrated in the example can be used in conjunction with any of the systems, methods, or devices shown in the figure, as well as other devices. In various embodiments, some of the method elements shown may be executed concurrently in a different order than shown, or may be omitted. Additional method elements may also be executed as needed. As shown, the method can operate as follows.

[0207] At step 1902, a user equipment (UE) such as UE 106 may establish one or more wireless local area network (WLAN) connections with one or more UEs via a discovery process to form a connected UE.

[0208] At step 1904, the UE may send capability information of each of the one or more connected UEs to the base station (BS) in the first control message, wherein the capability information includes one or more of the following: UE identifier (ID), indication of whether the UE of each connection is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU.

[0209] At step 1906, the UE may receive configuration information from the BS in a second control message for reporting data from one or more connected UEs.

[0210] At step 1908, the UE may send configuration information to each of the one or more connected UEs via one or more WLAN connections.

[0211] At step 1910, the UE may receive data from each of the one or more connected UEs via one or more WLAN connections based on configuration information.

[0212] At step 1912, the UE may send data collected from each of the one or more connected UEs to the BS via a third control message for use in one or more artificial intelligence (AI) models.

[0213] In some cases, a UE may perform the discovery process by: sending a discovery message to one or more UEs, wherein the discovery message includes an AEU identifier and AEU capability information; receiving a discovery message response from one or more UEs to establish a WLAN connection based on the discovery message; sending a connection establishment request to one or more UEs based on the discovery message response; receiving a connection establishment request response from one or more UEs to establish a WLAN connection based on the connection establishment request; and / or storing capability information received from each of the one or more connected UEs at the AEU.

[0214] In one aspect, the WLAN connection includes one or more Radio Access Technologies (RATs) and one or more peer-to-peer (P2P) connections. Additionally, the first control message may include an indication identifying each of the one or more UEs connected to the AEU and the type of WLAN connection of the UEs connected to the AEU. The second control message may include: 1) a first transparent container including configuration information, specifically data collection and reporting configuration information for each of the one or more connected UEs; and 2) a second transparent container including one or more AI models.

[0215] In some cases, the UE may receive configuration information from the BS in a second control message via at least one of Radio Resource Control (RRC) messages, Layer 1 (L1) signaling, Layer 2 (L2) signaling, or Layer 3 (L3) signaling. In some cases, the UE may receive from the BS in a second control message one or more AI models for local training and inference for each of the one or more connected UEs.

[0216] In one example, the third control message may include: a first transparent container comprising data received from each of the UEs connected to one or more connections of the AEU based on configuration information; a second transparent container comprising AI model training results based on data from each of the UEs connected to the AEU; a third transparent container comprising AI model inference results based on data from each of the UEs connected to the AEU; a fourth transparent container comprising aggregated AI model training results obtained by combining each of the AI ​​model training results and AI model inference results from each of the UEs connected to the AEU; and / or a fifth transparent container comprising aggregated inference results obtained using the aggregated AI model training results.

[0217] In some cases, a UE may establish a network connection with a base station (BS); establish one or more peer-to-peer or WLAN connections with one or more user equipment (UEs) to form a UE connected outside the coverage of the BS; send capability information of each of the one or more connected UEs to the BS in a first control message, wherein the capability information includes one or more of the following: UE identifier (ID), indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with an AEU, or a second confirmation indicating approval or disapproval of cooperation with an AEU; receive one or more AI models from the BS for local training and inference in a second control message; send one or more AI models to each of the one or more connected UEs via one or more peer-to-peer or WLAN connections; receive local training or inference results from each of the one or more connected UEs via one or more peer-to-peer or WLAN connections; aggregate the local training or inference results from the one or more connected UEs; and send the aggregated results from additional local training or inference to the BS via a third control message. Peer-to-peer or WLAN connections include one or more radio access technologies (RATs) and one or more peer-to-peer (P2P) connections.

[0218] In one example, the first control information also includes an indication of the type of peering or WLAN connection of each of the one or more connected UEs connected to the AEU. In one example, the second control message also includes: a first transparent container including configuration information, the first transparent container including data collection and reporting configuration information of each of the one or more connected UEs; and a second transparent container including one or more AI models.

[0219] In some cases, the UE may receive configuration information from the BS in a second control message via at least one of Radio Resource Control (RRC) messages, Layer 1 (L1) signaling, Layer 2 (L2) signaling, or Layer 3 (L3) signaling. In some cases, the UE may receive from the BS in a second control message one or more AI models for local training and inference for each of the one or more connected UEs.

[0220] The third control message further includes: a first transparent container, which includes data received from each of the UEs connected to the AEU based on configuration information; a second transparent container, which includes AI model training results based on data from each of the UEs connected to the AEU; a third transparent container, which includes AI model inference results based on data from each of the UEs connected to the AEU; a fourth transparent container, which includes aggregated AI model training results obtained by combining each of the AI ​​model training results and AI model inference results from each of the UEs connected to the AEU; and a fifth transparent container, which includes aggregated inference results obtained using the aggregated AI model training results.

[0221] As is widely recognized, the use of personally identifiable information should comply with privacy policies and practices that are generally accepted to meet or exceed industry or governmental requirements for protecting user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of authorized use should be clearly explained to users.

[0222] Embodiments of this disclosure may be implemented in any of a variety of forms. For example, some embodiments may be implemented as a computer-implemented method, a computer-readable storage medium, or a computer system. Other embodiments may be implemented using one or more custom-designed hardware devices such as ASICs. Other embodiments may be implemented using one or more programmable hardware elements such as FPGAs.

[0223] In some embodiments, a non-transitory computer-readable storage medium may be configured to store program instructions and / or data, wherein, if executed by a computer system, the program instructions cause the computer system to perform a method, such as any method embodiment of the method embodiments described herein, or any combination of method embodiments described herein, or any subset or combination of any such subset of any method embodiments described herein.

[0224] In some implementations, the device (e.g., UE 106) may be configured to include a processor (or a set of processors) and a memory medium, the processor including one or more baseband processors and one or more application processors, wherein the memory medium stores program instructions, and the processor is configured to read from the memory medium and execute the program instructions, wherein the program instructions are executable to implement any of the various method implementations described herein (or any combination of the method implementations described herein, or any subset of any of the method implementations described herein, or any combination of such subsets). The device may be implemented in any of the various forms.

[0225] By interpreting each message / signal X received by the user equipment (UE) in the downlink as a message / signal X sent by the base station, and interpreting each message / signal Y sent by the UE in the uplink as a message / signal Y received by the base station, any of the methods described herein for operating the UE can serve as the basis for a corresponding method for operating the base station.

[0226] Although the above embodiments have been described in considerable detail, many variations and modifications will become apparent to those skilled in the art once the above disclosure is fully understood. It is intended that the following claims be construed as encompassing all such variations and modifications.

Claims

1. An apparatus for configuring an AI edge user equipment (AUE) to enhance the performance of an artificial intelligence (AI) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: UEs that establish one or more peer connections with one or more UEs through a discovery process to form a connection; In the first control message, capability information of each of the one or more connected UEs is sent to the base station (BS), wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU. In the second control message, configuration information for reporting data from the UEs of the one or more connections is received from the BS; The configuration information is sent to each of the one or more connected UEs via the one or more peer connections; Based on the configuration information, data is received from each of the one or more connected UEs via the one or more peer connections; as well as The data from at least one of the connected UEs is sent to the BS via a third control message for use in one or more artificial intelligence (AI) models.

2. The apparatus of claim 1, wherein the one or more processors are further configured to perform the discovery process by: Send a discovery message to the one or more UEs, wherein the discovery message includes an AEU identifier and AEU capability information; Based on the discovery message, receive a discovery message response from the one or more UEs for establishing the peer connection; Based on the discovery message response, a connection establishment request is sent to the one or more UEs; Based on the connection establishment request, receive a connection establishment request response from one or more UEs to establish the peer connection; as well as The capability information received from each of the one or more connected UEs is stored at the AEU.

3. The apparatus of claim 1, wherein the peering connection comprises one or more radio access technologies (RATs) and one or more wireless local area network (WLAN) connections.

4. The apparatus of claim 1, wherein the first control message further includes an indication identifying each of the UEs connected to the AEU and the type of peer connection of the UEs connected to the AEU.

5. The apparatus according to claim 1, wherein the second control message further comprises: A first transparent container, the first transparent container including the configuration information, the first transparent container including data collection and reporting configuration information for each of the one or more connected UEs; and The second transparent container includes one or more AI models.

6. The apparatus of claim 1, wherein the one or more processors are further configured to receive the configuration information from the BS in the second control message via at least one of a Radio Resource Control (RRC) message, Layer 1 (L1) signaling, Layer 2 (L2) signaling, or Layer 3 (L3) signaling.

7. The apparatus of claim 1, wherein the one or more processors are further configured to: receive, in the second control message, one or more AI models for local training and inference for each of the one or more connected UEs from the BS.

8. The apparatus of claim 1, wherein the third control message further comprises: A first transparent container, the first transparent container including the data received from each of the one or more UEs connected to the AEU based on the configuration information; A second transparent container, comprising the results of AI model training based on the data from each of the one or more connected UEs; A third transparent container, the third transparent container comprising AI model inference results based on the data from each of the one or more connected UEs; The fourth transparent container includes an aggregated AI model training result obtained by combining each of the AI ​​model training results and the AI ​​model inference results from each of the one or more connected UEs. and The fifth transparent container includes the inference results of the aggregation obtained using the training results of the AI ​​model of the aggregation.

9. An apparatus for configuring a user equipment (UE) to enhance the performance of an artificial intelligence (AI) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: Send a registration request to the base station (BS); Receive configuration information for the artificial intelligence (AI) server from the BS; When the UE is outside the coverage of the BS, it establishes one or more secondary connections to the AI ​​server based on the configuration information; and At least one of the AI ​​data, AI model, or AI training or inference results of the AI ​​server is transferred to the AI ​​server via secondary connectivity.

10. The apparatus of claim 9, wherein the configuration information includes one or more of the Internet Protocol (IP) address of the AI ​​server and the security credentials of the AI ​​server.

11. The apparatus of claim 9, wherein the secondary connectivity includes at least one of wireless local area network (WLAN) connectivity or second subscriber identity module (SIM) connectivity.

12. The apparatus of claim 9, wherein the one or more processors are further configured to: Determine that the UE is disconnected from the BS; and The one or more secondary connections to the AI ​​server are established based on the disconnection of the UE from the BS.

13. An apparatus for configuring an AI edge user equipment (AUE) to enhance the performance of an artificial intelligence (AI) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: Establish a network connection with the base station (BS); Establish one or more wireless local area network (WLAN) connections with one or more user equipment (UEs) to form a connection for the UE outside the coverage of the BS; In the first control message, the BS sends capability information of each of the one or more connected UEs, wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU. In the second control message, one or more AI models for local training and inference are received from the BS; The one or more AI models are sent to each of the one or more connected UEs via the one or more WLAN connections; Receive local training or inference results from each of the one or more connected UEs via the one or more WLAN connections; Aggregate the local training or inference results from the one or more connected UEs; as well as The aggregated results from the local training or inference are sent to the BS via a third control message.

14. The apparatus of claim 13, wherein the WLAN connection comprises one or more radio access technologies (RATs) and one or more peer-to-peer (P2P) connections.

15. The apparatus of claim 13, wherein the first control message further includes an indication identifying the type of WLAN connection of each of the one or more UEs connected to the AEU and the one or more UEs connected to the AEU.

16. The apparatus of claim 13, wherein the second control message further comprises: A first transparent container, the first transparent container including the configuration information, the first transparent container including data collection and reporting configuration information for each of the one or more connected UEs; and The second transparent container includes one or more AI models.

17. The apparatus of claim 13, wherein the one or more processors are further configured to receive the configuration information from the BS in the second control message via at least one of a Radio Resource Control (RRC) message, Layer 1 (L1) signaling, Layer 2 (L2) signaling, or Layer 3 (L3) signaling.

18. The apparatus of claim 13, wherein the one or more processors are further configured to: receive, in the second control message, one or more AI models for local training and inference for each of the one or more connected UEs from the BS.

19. The apparatus of claim 13, wherein the third control message further comprises: A first transparent container, the first transparent container including data received from each of the one or more UEs connected to the AEU based on the configuration information; A second transparent container, comprising the results of AI model training based on the data from each of the one or more connected UEs; A third transparent container, the third transparent container comprising AI model inference results based on the data from each of the one or more connected UEs; The fourth transparent container includes an aggregated AI model training result obtained by combining each of the AI ​​model training results and the AI ​​model inference results from each of the one or more connected UEs. and The fifth transparent container includes the inference results of the aggregation obtained using the training results of the AI ​​model of the aggregation.

20. An apparatus for configuring an AI edge user equipment (AUE) to enhance the performance of an artificial intelligence (AI) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: Establish a network connection with the base station (BS); Establish one or more wireless local area network (WLAN) connections with one or more user equipment (UEs) to form a connection for the UE outside the coverage of the BS; Data for one or more artificial intelligence (AI) models is collected from the UEs of the one or more connected WLAN connections. as well as The data from the UE of the one or more connections is used to perform at least one of the following: local training using the one or more AI models, or receiving local inference from the one or more AI models.

21. The apparatus of claim 20, wherein the WLAN connection includes one or more radio access technologies (RATs) among various radio access technologies (RATs).

22. The apparatus of claim 20, wherein collecting data for the one or more AI models from a UE via the one or more WLAN connections further comprises: Collect at least one of the training results from the aggregation of the local training or the inference results from the aggregation of the local inference.

23. A method for enhancing the performance of an artificial intelligence (AI) network by leveraging peer-to-peer connections, the method comprising: The UE establishes one or more wireless local area network (WLAN) connections with one or more UEs through a discovery process to form a connected UE; In the first control message, capability information of each of the one or more connected UEs is sent to the base station (BS), wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU. In the second control message, configuration information for reporting data from the UEs of the one or more connections is received from the BS; The configuration information is sent to each of the one or more connected UEs via the one or more WLAN connections; Based on the configuration information, data is received from each of the one or more connected UEs via the one or more WLAN connections; as well as The data from at least one of the connected UEs is sent to the BS via a third control message for use in one or more artificial intelligence (AI) models.

24. A computer program product comprising a non-transitory computer-readable storage medium embodying computer-readable program code adapted to be executed to implement a method for providing a sidelink to an artificial intelligence (AI) network, the method comprising: The UE establishes one or more wireless local area network (WLAN) connections with one or more UEs through a discovery process to form a connected UE; In the first control message, capability information of each of the one or more connected UEs is sent to the base station (BS), wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU. In the second control message, configuration information for reporting data from the UEs of the one or more connections is received from the BS; The configuration information is sent to each of the one or more connected UEs via the one or more WLAN connections; Based on the configuration information, data is received from each of the one or more connected UEs via the one or more WLAN connections; as well as Data collected from at least one of the connected UEs is sent to the BS via a third control message for use in one or more artificial intelligence (AI) models.

25. An apparatus for configuring an AI edge user equipment (AEU) to enhance the performance of an artificial intelligence (AI) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: Establish one or more wireless local area network (WLAN) connections with one or more UEs through the discovery process; In the first control message, capability information of each of the one or more connected UEs is sent to the base station (BS), wherein the capability information includes one or more of the following: UE identifier (ID), an indication of whether each connected UE is outside the coverage of the BS, a first confirmation indicating approval or disapproval of sharing data with the AEU, or a second confirmation indicating approval or disapproval of cooperating with the AEU. In the second control message, configuration information for reporting data from the UEs of the one or more connections is received from the BS; The configuration information is sent to each of the one or more connected UEs via the one or more WLAN connections; Based on the configuration information, data is received from each of the one or more connected UEs via the one or more WLAN connections; as well as Data collected from each of the one or more connected UEs is sent to the BS via a third control message for use in one or more artificial intelligence (AI) models.

26. An apparatus for selecting user equipment (UE) to enhance the performance of an artificial intelligence / machine learning (AI / ML) network, the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: Receive a request message from the UE, wherein the request message includes UE capabilities and discovery information; The UE capabilities and the discovery information are compared with the AEU's capabilities, policies, and available resources; Determine the reference signal received power (RSRP) of the UE; The UE is selected based on the RSRP exceeding the RSRP threshold offset value; as well as Send an accept message to the UE to establish a connection.

27. The apparatus of claim 26, wherein the one or more processors are further configured to: send to the base station (BS) an indication that the AEU satisfies the capability and the available resources indicated in the request message from the UE.

28. The apparatus of claim 26, wherein the one or more processors are further configured to: receive from a base station (BS) a configuration indicating to select the AEU for connection with the UE based on a specific AEU-UE pairing.

29. The apparatus of claim 26, wherein selecting the UE further comprises: The UE is selected only if the available computing resources of the device meet the resources required by the UE.

30. The apparatus of claim 26, wherein the one or more processors are further configured to: receive from the UE an indication that the AEU is a preferred AEU for connecting to the UE.

31. A user equipment (UE) configured to perform any of the operations described herein.

32. A base station (BS) configured to perform any of the operations described herein.

33. A computer program product comprising computer instructions that, when executed by one or more processors, perform any of the operations described herein.

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